{smcl}
{txt}{sf}{ul off}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Output\Hardwiring Committment.APPENDIX G.04-21-2023.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}22 Apr 2023, 09:56:28
{txt}
{com}. 
. 
. 
. *******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. 
. **** APPENDIX G STATISTICAL ANALYSES: REPLICATE MANUSCRIPT MODELS -- SPLIT INTO 1ST YEAR ADMINISTRATION NOMINATED APPOINTEES [if okstartadyr==1: N = 370, 43.02% OF FULL SAMPLE] VERSUS NON-1ST YEAR ///
> **** ADMINISTRATION NOMINATED APPOINTEES [if okstartadyr!=1: N = 490, 56.98% OF FULL SAMPLE] ****
. 
. use "C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Data\Krause and Byers.SRD.06-03-2022.dta", replace
{txt}
{com}. 
. 
. 
. *** DESCRIPTIVE STATISTICS OF SPLIT PRESIDENTIAL SUBSAMPLES BY NUMBER OF TERMS]: EMPLOY IN CALCULATING MARGINAL EFFECTS FROM REGRESSION MODELS BELOW ***
. 
. sum zloyalmedian if okstartadyr==1, detail

                        {txt}zloyalmedian
{hline 61}
      Percentiles      Smallest
 1%    {res}-1.697527      -1.786184
{txt} 5%    {res}-1.172676      -1.743629
{txt}10%    {res}-.6008357       -1.73299       {txt}Obs         {res}        370
{txt}25%    {res}  -.36944      -1.697527       {txt}Sum of Wgt. {res}        370

{txt}50%    {res}-.1070143                      {txt}Mean          {res} .2471454
                        {txt}Largest       Std. Dev.     {res} .9611896
{txt}75%    {res} 1.049077       2.247725
{txt}90%    {res} 1.862952       2.286734       {txt}Variance      {res} .9238854
{txt}95%    {res} 1.967567       2.409081       {txt}Skewness      {res} .5347874
{txt}99%    {res} 2.247725       2.731794       {txt}Kurtosis      {res} 2.488397
{txt}
{com}. 
. sum zloyalmedian if okstartadyr!=1, detail

                        {txt}zloyalmedian
{hline 61}
      Percentiles      Smallest
 1%    {res}-1.717032      -1.844698
{txt} 5%    {res}-1.374814      -1.825194
{txt}10%    {res}-.6930394      -1.816328       {txt}Obs         {res}        490
{txt}25%    {res}-.4119956      -1.811008       {txt}Sum of Wgt. {res}        490

{txt}50%    {res}-.2284749                      {txt}Mean          {res} .0388578
                        {txt}Largest       Std. Dev.     {res} .8615826
{txt}75%    {res} .3859205       2.299146
{txt}90%    {res} 1.220186       2.329289       {txt}Variance      {res} .7423245
{txt}95%    {res} 1.827489       2.331063       {txt}Skewness      {res} .7028129
{txt}99%    {res} 2.134243       2.508377       {txt}Kurtosis      {res} 3.248432
{txt}
{com}. 
. 
. 
. 
. ** GENERATE CENSORING VARIABLE FOR HOLDOVER APPOINTEES SERVING BETWEEN/ACROSS ADMINISTRATIONS [=1]; UNCENSRED OBSERVATIONS [=0] ** 
. 
. gen singleadmin_service=1 if holdover==0
{txt}(29 missing values generated)

{com}. *
. replace singleadmin_service=0 if holdover==1
{txt}(29 real changes made)

{com}. *
. *
. tab singleadmin_service

{txt}singleadmin {c |}
   _service {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         29        3.37        3.37
{txt}          1 {c |}{res}        831       96.63      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        860      100.00
{txt}
{com}. 
. 
. ** SET FOR SURVIVAL DATA WITH A SINGLE RECORD PER APPOINTEE OBSERVATION [N = 860: UNCENSORED N = 831; CENSORED N = 29] ** 
. stset okapptdur, failure(singleadmin_service)

     {txt}failure event:  {res}singleadmin_service != 0 & singleadmin_service < .
{txt}obs. time interval:  {res}(0, okapptdur]
{txt} exit on or before:  {res}failure

{txt}{hline 78}
{res}        860{txt}  total observations
{res}          0{txt}  exclusions
{hline 78}
{res}        860{txt}  observations remaining, representing
{res}        831{txt}  failures in single-record/single-failure data
{res}    850,034{txt}  total analysis time at risk and under observation
                                                at risk from t = {res}        0
                                     {txt}earliest observed entry t = {res}        0
                                          {txt}last observed exit t = {res}    4,074
{txt}
{com}. *
. 
. 
. 
. 
. *******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. *
. *
. *
. *
. 
. ** ESTIMATE COX SEMIPARAMETRIC AND WEIBULL PARAMETRIC MODELS PRESENTED IN MANUSCRIPT [MODELS H1A - H4B] ** 
. 
. 
. ** NOTE COVARIATES THAT VARY TRHOUGH TIME ARE BASED ON THE STARTING DATE OF APPOINTED SERVICE [I.E., "OKSTART....""]
. 
. 
.  **** ORIGINAL-FULL SAMPLE (N = 860); ONLY YEAR 1 NOMINEES (N = 370 -- "HA" MODELS); NON-YEAR 1 NOMINEES (N = 490) --  "HB" MODELS] ***
. 
. 
. 
. ****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. 
. *** MANUSCRIPT-BASED SURVIVAL REGRESSION ANALYSES: COX SEMIPARAMETRIC & WEIBULL PARAMETRIC MODELS ****
. 
. 
. 
. 
. ****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. **** APPENDIX G REGRESSION MODELS  ***
. 
. 
. 
. 
. **** MODEL G1A: COX MODEL [OMISSION OF BOTH AGENCY AND PRESIDENTIAL ADMINISTRATION FIXED EFFECTS: CLUSTER-ADJUSTED STANDARD ERRORS BY AGENCY] ****
. 
. stcox   c.zloyalmedian##i.soubinaryagency2nom  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp  okstartunemployment   if okstartadyr==1,  hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur

{txt}Iteration 0:   log pseudolikelihood = {res}-1679.0195
{txt}Iteration 1:   log pseudolikelihood = {res}-1577.3215
{txt}Iteration 2:   log pseudolikelihood = {res}-1574.8332
{txt}Iteration 3:   log pseudolikelihood = {res}-1574.8262
{txt}Iteration 4:   log pseudolikelihood = {res}-1574.8262
{txt}Refining estimates:
Iteration 0:   log pseudolikelihood = {res}-1574.8262

{txt}Cox regression -- Breslow method for ties

No. of subjects      = {res}         370             {txt}Number of obs    =  {res}       370
{txt}No. of failures      = {res}         341
{txt}Time at risk         = {res}      433121
                                                {txt}Wald chi2({res}15{txt})    =  {res}    347.81
{txt}Log pseudolikelihood =   {res}-1574.8262             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 100:(Std. Err. adjusted for {res:39} clusters in sbagency)}
{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}                                _t{col 36}{c |} Haz. Ratio{col 48}   Std. Err.{col 60}      z{col 68}   P>|z|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}zloyalmedian {c |}{col 36}{res}{space 2} 1.611823{col 48}{space 2} .1787001{col 59}{space 1}    4.31{col 68}{space 3}0.000{col 76}{space 4} 1.297018{col 89}{space 3} 2.003036
{txt}{space 13}1.soubinaryagency2nom {c |}{col 36}{res}{space 2} 1.466024{col 48}{space 2} .2315171{col 59}{space 1}    2.42{col 68}{space 3}0.015{col 76}{space 4} 1.075766{col 89}{space 3} 1.997857
{txt}{space 34} {c |}
soubinaryagency2nom#c.zloyalmedian {c |}
{space 32}1  {c |}{col 36}{res}{space 2} .6015777{col 48}{space 2} .0823184{col 59}{space 1}   -3.71{col 68}{space 3}0.000{col 76}{space 4}  .460061{col 89}{space 3} .7866255
{txt}{space 34} {c |}
{space 21}zpecompmedian {c |}{col 36}{res}{space 2} 1.025427{col 48}{space 2} .0796482{col 59}{space 1}    0.32{col 68}{space 3}0.746{col 76}{space 4} .8806209{col 89}{space 3} 1.194043
{txt}{space 21}zmecompmedian {c |}{col 36}{res}{space 2} .8635388{col 48}{space 2} .0653794{col 59}{space 1}   -1.94{col 68}{space 3}0.053{col 76}{space 4} .7444518{col 89}{space 3} 1.001676
{txt}{space 25}toplevel2 {c |}{col 36}{res}{space 2}  .486461{col 48}{space 2} .0691363{col 59}{space 1}   -5.07{col 68}{space 3}0.000{col 76}{space 4} .3681921{col 89}{space 3} .6427198
{txt}{space 14}presagencyideolalign {c |}{col 36}{res}{space 2} 1.099346{col 48}{space 2} .1971266{col 59}{space 1}    0.53{col 68}{space 3}0.597{col 76}{space 4} .7735759{col 89}{space 3} 1.562304
{txt}{space 12}presagencyideolopposed {c |}{col 36}{res}{space 2} 1.200682{col 48}{space 2} .2056265{col 59}{space 1}    1.07{col 68}{space 3}0.286{col 76}{space 4} .8583271{col 89}{space 3} 1.679589
{txt}{space 19}subagencydesign {c |}{col 36}{res}{space 2}  1.43058{col 48}{space 2} .2429893{col 59}{space 1}    2.11{col 68}{space 3}0.035{col 76}{space 4} 1.025492{col 89}{space 3} 1.995684
{txt}{space 12}standaloneagencydesign {c |}{col 36}{res}{space 2} 1.002983{col 48}{space 2} .1654692{col 59}{space 1}    0.02{col 68}{space 3}0.986{col 76}{space 4} .7258801{col 89}{space 3}  1.38587
{txt}{space 8}okstartsenpolarizationmean {c |}{col 36}{res}{space 2} .0113626{col 48}{space 2} .0463835{col 59}{space 1}   -1.10{col 68}{space 3}0.273{col 76}{space 4} 3.81e-06{col 89}{space 3} 33.89902
{txt}{space 11}okstartfilipresdistance {c |}{col 36}{res}{space 2} 2.203696{col 48}{space 2} .9249373{col 59}{space 1}    1.88{col 68}{space 3}0.060{col 76}{space 4} .9680205{col 89}{space 3} 5.016707
{txt}{space 23}okcrossover {c |}{col 36}{res}{space 2} .1934905{col 48}{space 2} .0359149{col 59}{space 1}   -8.85{col 68}{space 3}0.000{col 76}{space 4} .1344817{col 89}{space 3} .2783916
{txt}{space 20}okstartpresapp {c |}{col 36}{res}{space 2} 1.009132{col 48}{space 2} .0070188{col 59}{space 1}    1.31{col 68}{space 3}0.191{col 76}{space 4} .9954686{col 89}{space 3} 1.022983
{txt}{space 15}okstartunemployment {c |}{col 36}{res}{space 2} .9564226{col 48}{space 2} .0707936{col 59}{space 1}   -0.60{col 68}{space 3}0.547{col 76}{space 4} .8272649{col 89}{space 3} 1.105745
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       370{col 28} -1679.02{col 39}-1574.826{col 50}    15{col 58} 3179.652{col 69} 3238.355
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. estimates store modelG1A
{txt}
{com}. estout modelG1A, cells(b(star fmt(3)) se(par fmt(3))) eform
{res}
{txt}{hline 28}
{txt}                 modelG1A   
{txt}                     b/se   
{txt}{hline 28}
{txt}zloyalmedian{res}        1.612***{txt}
            {res}      (0.179)   {txt}
{txt}0.soubinar~m{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}1.soubinar~m{res}        1.466*  {txt}
            {res}      (0.232)   {txt}
{txt}0.soubinar~i{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}1.soubinar~i{res}        0.602***{txt}
            {res}      (0.082)   {txt}
{txt}zpecompmed~n{res}        1.025   {txt}
            {res}      (0.080)   {txt}
{txt}zmecompmed~n{res}        0.864   {txt}
            {res}      (0.065)   {txt}
{txt}toplevel2   {res}        0.486***{txt}
            {res}      (0.069)   {txt}
{txt}presagency~n{res}        1.099   {txt}
            {res}      (0.197)   {txt}
{txt}presagency~d{res}        1.201   {txt}
            {res}      (0.206)   {txt}
{txt}subagencyd~n{res}        1.431*  {txt}
            {res}      (0.243)   {txt}
{txt}standalone~n{res}        1.003   {txt}
            {res}      (0.165)   {txt}
{txt}okstartsen~n{res}        0.011   {txt}
            {res}      (0.046)   {txt}
{txt}okstartfil~e{res}        2.204   {txt}
            {res}      (0.925)   {txt}
{txt}okcrossover {res}        0.193***{txt}
            {res}      (0.036)   {txt}
{txt}okstartpre~p{res}        1.009   {txt}
            {res}      (0.007)   {txt}
{txt}okstartune~t{res}        0.956   {txt}
            {res}      (0.071)   {txt}
{txt}{hline 28}

{com}. 
. 
. *** COMPUTE Figure G1A: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the HAZARD RATIO of APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [MG1A−MG4A] × 1 Horizontal Point Estimates and 95% CIs{c )-}{c )-}. ****
. *** NOTE: IQR = 1.418517 [1.049077 - (-0.36944)] ***
. 
.  
. 
. ** ONE INTERQUARTILE RANGE MARGINAL EFFECT INCREASE IN APPOINTEE LOYALTY DIFFERENTIAL BETWEEN POLICY PRIORITY AGENCY VERSUS NON-POLICY PRIORITY AGENCY [FIGURE H1] **
. 
. lincomest 1.soubinaryagency2nom#c.zloyalmedian*1.418517, eform(hr)
{txt}Confidence interval for formula:
{res}1.soubinaryagency2nom#c.zloyalmedian*1.418517

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .4863193{col 26}{space 2} .0943977{col 37}{space 1}   -3.71{col 46}{space 3}0.000{col 54}{space 4} .3324283{col 67}{space 3} .7114513
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelG1Azloyal = r(table)
{txt}
{com}. mat list modelG1Azloyal
{res}
{txt}modelG1Azloyal[9,1]
              (1)
     b {res} .48631935
{txt}    se {res} .09439767
{txt}     z {res} -3.713891
{txt}pvalue {res}  .0002041
{txt}    ll {res} .33242826
{txt}    ul {res} .71145127
{txt}    df {res}         .
{txt}  crit {res}  1.959964
{txt} eform {res}         1
{reset}
{com}. *
. 
. 
. ******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. **** MODEL G1B: COX MODEL [OMISSION OF BOTH AGENCY AND PRESIDENTIAL ADMINISTRATION FIXED EFFECTS: CLUSTER-ADJUSTED STANDARD ERRORS BY AGENCY] ****
. 
. stcox   c.zloyalmedian##i.soubinaryagency2nom  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp  okstartunemployment  i.okstartadyr  if okstartadyr!=1,  hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur

{txt}Iteration 0:   log pseudolikelihood = {res}-2550.1086
{txt}Iteration 1:   log pseudolikelihood = {res}-2397.0653
{txt}Iteration 2:   log pseudolikelihood = {res}-2392.0284
{txt}Iteration 3:   log pseudolikelihood = {res}-2392.0167
{txt}Iteration 4:   log pseudolikelihood = {res}-2392.0167
{txt}Refining estimates:
Iteration 0:   log pseudolikelihood = {res}-2392.0167

{txt}Cox regression -- Breslow method for ties

No. of subjects      = {res}         490             {txt}Number of obs    =  {res}       490
{txt}No. of failures      = {res}         490
{txt}Time at risk         = {res}      416913
                                                {txt}Wald chi2({res}21{txt})    =  {res}   1544.26
{txt}Log pseudolikelihood =   {res}-2392.0167             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 100:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}                                _t{col 36}{c |} Haz. Ratio{col 48}   Std. Err.{col 60}      z{col 68}   P>|z|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}zloyalmedian {c |}{col 36}{res}{space 2} 1.238985{col 48}{space 2} .1651693{col 59}{space 1}    1.61{col 68}{space 3}0.108{col 76}{space 4} .9540958{col 89}{space 3}  1.60894
{txt}{space 13}1.soubinaryagency2nom {c |}{col 36}{res}{space 2} .9910348{col 48}{space 2}  .109151{col 59}{space 1}   -0.08{col 68}{space 3}0.935{col 76}{space 4} .7986177{col 89}{space 3} 1.229812
{txt}{space 34} {c |}
soubinaryagency2nom#c.zloyalmedian {c |}
{space 32}1  {c |}{col 36}{res}{space 2} .7094306{col 48}{space 2} .1032677{col 59}{space 1}   -2.36{col 68}{space 3}0.018{col 76}{space 4} .5333416{col 89}{space 3} .9436575
{txt}{space 34} {c |}
{space 21}zpecompmedian {c |}{col 36}{res}{space 2}  .998358{col 48}{space 2} .0918994{col 59}{space 1}   -0.02{col 68}{space 3}0.986{col 76}{space 4} .8335521{col 89}{space 3} 1.195748
{txt}{space 21}zmecompmedian {c |}{col 36}{res}{space 2}   1.0907{col 48}{space 2} .0902908{col 59}{space 1}    1.05{col 68}{space 3}0.294{col 76}{space 4} .9273443{col 89}{space 3} 1.282833
{txt}{space 25}toplevel2 {c |}{col 36}{res}{space 2} .6234408{col 48}{space 2} .0605807{col 59}{space 1}   -4.86{col 68}{space 3}0.000{col 76}{space 4} .5153267{col 89}{space 3}  .754237
{txt}{space 14}presagencyideolalign {c |}{col 36}{res}{space 2} 1.797014{col 48}{space 2} .2303991{col 59}{space 1}    4.57{col 68}{space 3}0.000{col 76}{space 4}  1.39771{col 89}{space 3} 2.310393
{txt}{space 12}presagencyideolopposed {c |}{col 36}{res}{space 2} 1.520382{col 48}{space 2} .2357762{col 59}{space 1}    2.70{col 68}{space 3}0.007{col 76}{space 4} 1.121892{col 89}{space 3} 2.060414
{txt}{space 19}subagencydesign {c |}{col 36}{res}{space 2} .9931792{col 48}{space 2}  .169062{col 59}{space 1}   -0.04{col 68}{space 3}0.968{col 76}{space 4} .7114321{col 89}{space 3} 1.386506
{txt}{space 12}standaloneagencydesign {c |}{col 36}{res}{space 2} .7988543{col 48}{space 2} .0891308{col 59}{space 1}   -2.01{col 68}{space 3}0.044{col 76}{space 4} .6419426{col 89}{space 3} .9941204
{txt}{space 8}okstartsenpolarizationmean {c |}{col 36}{res}{space 2} .0002156{col 48}{space 2} .0006608{col 59}{space 1}   -2.75{col 68}{space 3}0.006{col 76}{space 4} 5.31e-07{col 89}{space 3} .0875421
{txt}{space 11}okstartfilipresdistance {c |}{col 36}{res}{space 2} 1.656146{col 48}{space 2} .9751145{col 59}{space 1}    0.86{col 68}{space 3}0.392{col 76}{space 4} .5223041{col 89}{space 3} 5.251382
{txt}{space 23}okcrossover {c |}{col 36}{res}{space 2}  .169386{col 48}{space 2} .0375336{col 59}{space 1}   -8.01{col 68}{space 3}0.000{col 76}{space 4} .1097143{col 89}{space 3} .2615122
{txt}{space 20}okstartpresapp {c |}{col 36}{res}{space 2} .9923529{col 48}{space 2} .0043158{col 59}{space 1}   -1.77{col 68}{space 3}0.078{col 76}{space 4}   .98393{col 89}{space 3} 1.000848
{txt}{space 15}okstartunemployment {c |}{col 36}{res}{space 2} .9512601{col 48}{space 2} .0732963{col 59}{space 1}   -0.65{col 68}{space 3}0.517{col 76}{space 4} .8179234{col 89}{space 3} 1.106333
{txt}{space 34} {c |}
{space 23}okstartadyr {c |}
{space 32}3  {c |}{col 36}{res}{space 2} 2.725624{col 48}{space 2} .4130881{col 59}{space 1}    6.62{col 68}{space 3}0.000{col 76}{space 4} 2.025164{col 89}{space 3} 3.668358
{txt}{space 32}4  {c |}{col 36}{res}{space 2} 2.491278{col 48}{space 2} .8926408{col 59}{space 1}    2.55{col 68}{space 3}0.011{col 76}{space 4}  1.23433{col 89}{space 3} 5.028207
{txt}{space 32}5  {c |}{col 36}{res}{space 2} .6249598{col 48}{space 2} .1232326{col 59}{space 1}   -2.38{col 68}{space 3}0.017{col 76}{space 4} .4246273{col 89}{space 3} .9198058
{txt}{space 32}6  {c |}{col 36}{res}{space 2} 1.341883{col 48}{space 2} .2351394{col 59}{space 1}    1.68{col 68}{space 3}0.093{col 76}{space 4} .9518269{col 89}{space 3} 1.891784
{txt}{space 32}7  {c |}{col 36}{res}{space 2} 2.584447{col 48}{space 2} .7762458{col 59}{space 1}    3.16{col 68}{space 3}0.002{col 76}{space 4} 1.434521{col 89}{space 3} 4.656165
{txt}{space 32}8  {c |}{col 36}{res}{space 2} 4.358245{col 48}{space 2} 1.327505{col 59}{space 1}    4.83{col 68}{space 3}0.000{col 76}{space 4} 2.399048{col 89}{space 3} 7.917432
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       490{col 28}-2550.109{col 39}-2392.017{col 50}    21{col 58} 4826.033{col 69} 4914.116
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. estimates store modelG1B
{txt}
{com}. estout modelG1B, cells(b(star fmt(3)) se(par fmt(3))) eform
{res}
{txt}{hline 28}
{txt}                 modelG1B   
{txt}                     b/se   
{txt}{hline 28}
{txt}zloyalmedian{res}        1.239   {txt}
            {res}      (0.165)   {txt}
{txt}0.soubinar~m{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}1.soubinar~m{res}        0.991   {txt}
            {res}      (0.109)   {txt}
{txt}0.soubinar~i{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}1.soubinar~i{res}        0.709*  {txt}
            {res}      (0.103)   {txt}
{txt}zpecompmed~n{res}        0.998   {txt}
            {res}      (0.092)   {txt}
{txt}zmecompmed~n{res}        1.091   {txt}
            {res}      (0.090)   {txt}
{txt}toplevel2   {res}        0.623***{txt}
            {res}      (0.061)   {txt}
{txt}presagency~n{res}        1.797***{txt}
            {res}      (0.230)   {txt}
{txt}presagency~d{res}        1.520** {txt}
            {res}      (0.236)   {txt}
{txt}subagencyd~n{res}        0.993   {txt}
            {res}      (0.169)   {txt}
{txt}standalone~n{res}        0.799*  {txt}
            {res}      (0.089)   {txt}
{txt}okstartsen~n{res}        0.000** {txt}
            {res}      (0.001)   {txt}
{txt}okstartfil~e{res}        1.656   {txt}
            {res}      (0.975)   {txt}
{txt}okcrossover {res}        0.169***{txt}
            {res}      (0.038)   {txt}
{txt}okstartpre~p{res}        0.992   {txt}
            {res}      (0.004)   {txt}
{txt}okstartune~t{res}        0.951   {txt}
            {res}      (0.073)   {txt}
{txt}2.okstarta~r{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}3.okstarta~r{res}        2.726***{txt}
            {res}      (0.413)   {txt}
{txt}4.okstarta~r{res}        2.491*  {txt}
            {res}      (0.893)   {txt}
{txt}5.okstarta~r{res}        0.625*  {txt}
            {res}      (0.123)   {txt}
{txt}6.okstarta~r{res}        1.342   {txt}
            {res}      (0.235)   {txt}
{txt}7.okstarta~r{res}        2.584** {txt}
            {res}      (0.776)   {txt}
{txt}8.okstarta~r{res}        4.358***{txt}
            {res}      (1.328)   {txt}
{txt}{hline 28}

{com}. 
. 
. *** COMPUTE Figure G1: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the HAZARD RATIO of APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [MG1B−MG4B] × 1 Horizontal Point Estimates and 95% CIs{c )-}{c )-}. ****
. ** NOTE: IQR =  0.7979161  [0.3859205 - (-0.4119956)] ***
. 
. 
. 
. ** ONE INTERQUARTILE RANGE MARGINAL EFFECT INCREASE IN APPOINTEE LOYALTY DIFFERENTIAL BETWEEN POLICY PRIORITY AGENCY VERSUS NON-POLICY PRIORITY AGENCY [FIGURE H1] **
. 
. lincomest 1.soubinaryagency2nom#c.zloyalmedian*0.7979161, eform(hr)
{txt}Confidence interval for formula:
{res}1.soubinaryagency2nom#c.zloyalmedian*0.7979161

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .7603939{col 26}{space 2} .0883183{col 37}{space 1}   -2.36{col 46}{space 3}0.018{col 54}{space 4} .6055824{col 67}{space 3} .9547815
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelG1Bzloyal = r(table)
{txt}
{com}. mat list modelG1Bzloyal
{res}
{txt}modelG1Bzloyal[9,1]
               (1)
     b {res}  .76039389
{txt}    se {res}  .08831826
{txt}     z {res} -2.3583584
{txt}pvalue {res}  .01835596
{txt}    ll {res}  .60558238
{txt}    ul {res}  .95478153
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          1
{reset}
{com}. *
. 
. 
. 
. 
. ******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. **** MODEL G2A: COX MODEL [INCLUSION OF BOTH AGENCY AND PRESIDENTIAL ADMINISTRATION FIXED EFFECTS: CLUSTER-ADJUSTED STANDARD ERRORS BY AGENCY] ****
. 
. stcox   c.zloyalmedian##i.soubinaryagency2nom  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp  okstartunemployment  i.sbagency reagan bush41 clinton bush43  if okstartadyr==1,  hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur

{txt}note: 27.sbagency omitted because of collinearity
note: 57.sbagency omitted because of collinearity
note: 61.sbagency omitted because of collinearity
note: bush43 omitted because of collinearity
Iteration 0:   log pseudolikelihood = {res}-1679.0195
{txt}Iteration 1:   log pseudolikelihood = {res}-1561.5741
{txt}Iteration 2:   log pseudolikelihood = {res}-1544.3116
{txt}Iteration 3:   log pseudolikelihood = {res}-1542.7022
{txt}Iteration 4:   log pseudolikelihood = {res}-1542.5427
{txt}Iteration 5:   log pseudolikelihood = {res}-1542.5391
{txt}Iteration 6:   log pseudolikelihood = {res}-1542.5391
{txt}Refining estimates:
Iteration 0:   log pseudolikelihood = {res}-1542.5391

{txt}Cox regression -- Breslow method for ties

No. of subjects      = {res}         370             {txt}Number of obs    =  {res}       370
{txt}No. of failures      = {res}         341
{txt}Time at risk         = {res}      433121
                                                {txt}Wald chi2({res}38{txt})    =  {res}  41120.18
{txt}Log pseudolikelihood =   {res}-1542.5391             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 100:(Std. Err. adjusted for {res:39} clusters in sbagency)}
{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}                                _t{col 36}{c |} Haz. Ratio{col 48}   Std. Err.{col 60}      z{col 68}   P>|z|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}zloyalmedian {c |}{col 36}{res}{space 2} 1.517139{col 48}{space 2} .2039606{col 59}{space 1}    3.10{col 68}{space 3}0.002{col 76}{space 4} 1.165713{col 89}{space 3} 1.974508
{txt}{space 13}1.soubinaryagency2nom {c |}{col 36}{res}{space 2} 1.136903{col 48}{space 2}  .261456{col 59}{space 1}    0.56{col 68}{space 3}0.577{col 76}{space 4} .7243874{col 89}{space 3} 1.784334
{txt}{space 34} {c |}
soubinaryagency2nom#c.zloyalmedian {c |}
{space 32}1  {c |}{col 36}{res}{space 2} .6271316{col 48}{space 2} .1054227{col 59}{space 1}   -2.78{col 68}{space 3}0.006{col 76}{space 4} .4510962{col 89}{space 3} .8718629
{txt}{space 34} {c |}
{space 21}zpecompmedian {c |}{col 36}{res}{space 2} 1.029977{col 48}{space 2} .1117685{col 59}{space 1}    0.27{col 68}{space 3}0.785{col 76}{space 4}  .832643{col 89}{space 3} 1.274078
{txt}{space 21}zmecompmedian {c |}{col 36}{res}{space 2} .8669888{col 48}{space 2} .0937046{col 59}{space 1}   -1.32{col 68}{space 3}0.187{col 76}{space 4} .7014798{col 89}{space 3} 1.071549
{txt}{space 25}toplevel2 {c |}{col 36}{res}{space 2} .3763344{col 48}{space 2} .0781264{col 59}{space 1}   -4.71{col 68}{space 3}0.000{col 76}{space 4} .2505335{col 89}{space 3} .5653039
{txt}{space 14}presagencyideolalign {c |}{col 36}{res}{space 2} .3643824{col 48}{space 2} .1036417{col 59}{space 1}   -3.55{col 68}{space 3}0.000{col 76}{space 4} .2086648{col 89}{space 3} .6363054
{txt}{space 12}presagencyideolopposed {c |}{col 36}{res}{space 2}  .395873{col 48}{space 2} .1137001{col 59}{space 1}   -3.23{col 68}{space 3}0.001{col 76}{space 4} .2254651{col 89}{space 3} .6950764
{txt}{space 19}subagencydesign {c |}{col 36}{res}{space 2}  4.11004{col 48}{space 2} .8427852{col 59}{space 1}    6.89{col 68}{space 3}0.000{col 76}{space 4}  2.74981{col 89}{space 3} 6.143125
{txt}{space 12}standaloneagencydesign {c |}{col 36}{res}{space 2} 5.842835{col 48}{space 2} 2.287106{col 59}{space 1}    4.51{col 68}{space 3}0.000{col 76}{space 4} 2.712884{col 89}{space 3} 12.58392
{txt}{space 8}okstartsenpolarizationmean {c |}{col 36}{res}{space 2} .0528007{col 48}{space 2} .6292201{col 59}{space 1}   -0.25{col 68}{space 3}0.805{col 76}{space 4} 3.79e-12{col 89}{space 3} 7.35e+08
{txt}{space 11}okstartfilipresdistance {c |}{col 36}{res}{space 2} .5647736{col 48}{space 2} 1.605112{col 59}{space 1}   -0.20{col 68}{space 3}0.841{col 76}{space 4} .0021514{col 89}{space 3} 148.2615
{txt}{space 23}okcrossover {c |}{col 36}{res}{space 2} .1309271{col 48}{space 2} .0369791{col 59}{space 1}   -7.20{col 68}{space 3}0.000{col 76}{space 4} .0752689{col 89}{space 3} .2277422
{txt}{space 20}okstartpresapp {c |}{col 36}{res}{space 2} 1.011803{col 48}{space 2}   .01414{col 59}{space 1}    0.84{col 68}{space 3}0.401{col 76}{space 4} .9844656{col 89}{space 3}   1.0399
{txt}{space 15}okstartunemployment {c |}{col 36}{res}{space 2} .7650709{col 48}{space 2} .2340093{col 59}{space 1}   -0.88{col 68}{space 3}0.381{col 76}{space 4} .4200955{col 89}{space 3} 1.393335
{txt}{space 34} {c |}
{space 26}sbagency {c |}
{space 32}2  {c |}{col 36}{res}{space 2} 8.344551{col 48}{space 2} 2.896356{col 59}{space 1}    6.11{col 68}{space 3}0.000{col 76}{space 4} 4.226246{col 89}{space 3} 16.47598
{txt}{space 32}3  {c |}{col 36}{res}{space 2}  4.39738{col 48}{space 2}  1.69545{col 59}{space 1}    3.84{col 68}{space 3}0.000{col 76}{space 4} 2.065406{col 89}{space 3}   9.3623
{txt}{space 32}4  {c |}{col 36}{res}{space 2} 1.243426{col 48}{space 2} .4393919{col 59}{space 1}    0.62{col 68}{space 3}0.538{col 76}{space 4} .6220552{col 89}{space 3} 2.485482
{txt}{space 32}5  {c |}{col 36}{res}{space 2} 2.202437{col 48}{space 2} .8707217{col 59}{space 1}    2.00{col 68}{space 3}0.046{col 76}{space 4} 1.014812{col 89}{space 3} 4.779929
{txt}{space 32}6  {c |}{col 36}{res}{space 2} 2.992667{col 48}{space 2} 1.133931{col 59}{space 1}    2.89{col 68}{space 3}0.004{col 76}{space 4} 1.424083{col 89}{space 3} 6.288995
{txt}{space 32}7  {c |}{col 36}{res}{space 2} 4.944519{col 48}{space 2} 1.538348{col 59}{space 1}    5.14{col 68}{space 3}0.000{col 76}{space 4} 2.687179{col 89}{space 3} 9.098117
{txt}{space 32}8  {c |}{col 36}{res}{space 2} 7.396858{col 48}{space 2} 2.626761{col 59}{space 1}    5.63{col 68}{space 3}0.000{col 76}{space 4} 3.687821{col 89}{space 3} 14.83627
{txt}{space 32}9  {c |}{col 36}{res}{space 2} 4.204048{col 48}{space 2} 1.402896{col 59}{space 1}    4.30{col 68}{space 3}0.000{col 76}{space 4} 2.185851{col 89}{space 3} 8.085645
{txt}{space 31}12  {c |}{col 36}{res}{space 2}  5.29195{col 48}{space 2} 1.826723{col 59}{space 1}    4.83{col 68}{space 3}0.000{col 76}{space 4} 2.690236{col 89}{space 3} 10.40977
{txt}{space 31}13  {c |}{col 36}{res}{space 2} 2.751725{col 48}{space 2} .9041394{col 59}{space 1}    3.08{col 68}{space 3}0.002{col 76}{space 4} 1.445187{col 89}{space 3} 5.239453
{txt}{space 31}14  {c |}{col 36}{res}{space 2}  4.25328{col 48}{space 2} 1.353975{col 59}{space 1}    4.55{col 68}{space 3}0.000{col 76}{space 4} 2.279058{col 89}{space 3} 7.937661
{txt}{space 31}15  {c |}{col 36}{res}{space 2} 3.766005{col 48}{space 2} 1.014478{col 59}{space 1}    4.92{col 68}{space 3}0.000{col 76}{space 4} 2.221193{col 89}{space 3} 6.385216
{txt}{space 31}16  {c |}{col 36}{res}{space 2} 2.425307{col 48}{space 2} .3351041{col 59}{space 1}    6.41{col 68}{space 3}0.000{col 76}{space 4} 1.849935{col 89}{space 3} 3.179633
{txt}{space 31}17  {c |}{col 36}{res}{space 2} 2.891921{col 48}{space 2}   .50702{col 59}{space 1}    6.06{col 68}{space 3}0.000{col 76}{space 4} 2.050932{col 89}{space 3} 4.077759
{txt}{space 31}18  {c |}{col 36}{res}{space 2} 5.757613{col 48}{space 2} 1.878444{col 59}{space 1}    5.37{col 68}{space 3}0.000{col 76}{space 4} 3.037628{col 89}{space 3} 10.91316
{txt}{space 31}19  {c |}{col 36}{res}{space 2} .5406209{col 48}{space 2} .1942542{col 59}{space 1}   -1.71{col 68}{space 3}0.087{col 76}{space 4} .2673264{col 89}{space 3} 1.093311
{txt}{space 31}20  {c |}{col 36}{res}{space 2} .2777435{col 48}{space 2} .0933013{col 59}{space 1}   -3.81{col 68}{space 3}0.000{col 76}{space 4} .1437815{col 89}{space 3} .5365185
{txt}{space 31}21  {c |}{col 36}{res}{space 2} .6817584{col 48}{space 2}  .093339{col 59}{space 1}   -2.80{col 68}{space 3}0.005{col 76}{space 4} .5213065{col 89}{space 3} .8915955
{txt}{space 31}22  {c |}{col 36}{res}{space 2} .0942941{col 48}{space 2} .0563388{col 59}{space 1}   -3.95{col 68}{space 3}0.000{col 76}{space 4} .0292354{col 89}{space 3} .3041307
{txt}{space 31}23  {c |}{col 36}{res}{space 2} .5489788{col 48}{space 2}  .133916{col 59}{space 1}   -2.46{col 68}{space 3}0.014{col 76}{space 4} .3403427{col 89}{space 3} .8855125
{txt}{space 31}24  {c |}{col 36}{res}{space 2} .1201511{col 48}{space 2} .0764576{col 59}{space 1}   -3.33{col 68}{space 3}0.001{col 76}{space 4} .0345199{col 89}{space 3} .4182018
{txt}{space 31}25  {c |}{col 36}{res}{space 2} 1.812867{col 48}{space 2} .2518989{col 59}{space 1}    4.28{col 68}{space 3}0.000{col 76}{space 4} 1.380673{col 89}{space 3}  2.38035
{txt}{space 31}26  {c |}{col 36}{res}{space 2} .8536443{col 48}{space 2} .2285963{col 59}{space 1}   -0.59{col 68}{space 3}0.555{col 76}{space 4} .5050504{col 89}{space 3} 1.442843
{txt}{space 31}27  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 31}28  {c |}{col 36}{res}{space 2} 1.786576{col 48}{space 2} .3309138{col 59}{space 1}    3.13{col 68}{space 3}0.002{col 76}{space 4} 1.242682{col 89}{space 3}  2.56852
{txt}{space 31}29  {c |}{col 36}{res}{space 2} 11.78016{col 48}{space 2} 5.839897{col 59}{space 1}    4.98{col 68}{space 3}0.000{col 76}{space 4} 4.458377{col 89}{space 3} 31.12619
{txt}{space 31}30  {c |}{col 36}{res}{space 2} 3.776958{col 48}{space 2} 1.716163{col 59}{space 1}    2.92{col 68}{space 3}0.003{col 76}{space 4} 1.550157{col 89}{space 3} 9.202558
{txt}{space 31}50  {c |}{col 36}{res}{space 2} 8.287954{col 48}{space 2}  2.54731{col 59}{space 1}    6.88{col 68}{space 3}0.000{col 76}{space 4} 4.537637{col 89}{space 3} 15.13787
{txt}{space 31}51  {c |}{col 36}{res}{space 2} 3.425401{col 48}{space 2} 1.357844{col 59}{space 1}    3.11{col 68}{space 3}0.002{col 76}{space 4} 1.575039{col 89}{space 3} 7.449574
{txt}{space 31}52  {c |}{col 36}{res}{space 2} 3.287238{col 48}{space 2} 1.157734{col 59}{space 1}    3.38{col 68}{space 3}0.001{col 76}{space 4} 1.648337{col 89}{space 3} 6.555657
{txt}{space 31}53  {c |}{col 36}{res}{space 2} 1.068886{col 48}{space 2} .1751709{col 59}{space 1}    0.41{col 68}{space 3}0.684{col 76}{space 4} .7752375{col 89}{space 3} 1.473763
{txt}{space 31}54  {c |}{col 36}{res}{space 2} 3.269914{col 48}{space 2} .9146826{col 59}{space 1}    4.24{col 68}{space 3}0.000{col 76}{space 4} 1.889873{col 89}{space 3} 5.657704
{txt}{space 31}56  {c |}{col 36}{res}{space 2} 2.198206{col 48}{space 2} .9701521{col 59}{space 1}    1.78{col 68}{space 3}0.074{col 76}{space 4} .9255517{col 89}{space 3} 5.220788
{txt}{space 31}57  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 31}58  {c |}{col 36}{res}{space 2} 1.641464{col 48}{space 2}  .472646{col 59}{space 1}    1.72{col 68}{space 3}0.085{col 76}{space 4} .9335439{col 89}{space 3} 2.886209
{txt}{space 31}59  {c |}{col 36}{res}{space 2} .1865085{col 48}{space 2} .0954269{col 59}{space 1}   -3.28{col 68}{space 3}0.001{col 76}{space 4} .0684198{col 89}{space 3} .5084112
{txt}{space 31}60  {c |}{col 36}{res}{space 2} .7954014{col 48}{space 2} .1010867{col 59}{space 1}   -1.80{col 68}{space 3}0.072{col 76}{space 4} .6200233{col 89}{space 3} 1.020386
{txt}{space 31}61  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 34} {c |}
{space 28}reagan {c |}{col 36}{res}{space 2} 1.978391{col 48}{space 2} 2.330725{col 59}{space 1}    0.58{col 68}{space 3}0.562{col 76}{space 4} .1965709{col 89}{space 3} 19.91155
{txt}{space 28}bush41 {c |}{col 36}{res}{space 2}  .794256{col 48}{space 2} .3055401{col 59}{space 1}   -0.60{col 68}{space 3}0.549{col 76}{space 4} .3736923{col 89}{space 3} 1.688134
{txt}{space 27}clinton {c |}{col 36}{res}{space 2} .9320354{col 48}{space 2} .2003232{col 59}{space 1}   -0.33{col 68}{space 3}0.743{col 76}{space 4} .6116218{col 89}{space 3} 1.420306
{txt}{space 28}bush43 {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       370{col 28} -1679.02{col 39}-1542.539{col 50}    38{col 58} 3161.078{col 69} 3309.791
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. estimates store modelG2A
{txt}
{com}. estout modelG2A, cells(b(star fmt(3)) se(par fmt(3))) eform
{res}
{txt}{hline 28}
{txt}                 modelG2A   
{txt}                     b/se   
{txt}{hline 28}
{txt}zloyalmedian{res}        1.517** {txt}
            {res}      (0.204)   {txt}
{txt}0.soubinar~m{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}1.soubinar~m{res}        1.137   {txt}
            {res}      (0.261)   {txt}
{txt}0.soubinar~i{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}1.soubinar~i{res}        0.627** {txt}
            {res}      (0.105)   {txt}
{txt}zpecompmed~n{res}        1.030   {txt}
            {res}      (0.112)   {txt}
{txt}zmecompmed~n{res}        0.867   {txt}
            {res}      (0.094)   {txt}
{txt}toplevel2   {res}        0.376***{txt}
            {res}      (0.078)   {txt}
{txt}presagency~n{res}        0.364***{txt}
            {res}      (0.104)   {txt}
{txt}presagency~d{res}        0.396** {txt}
            {res}      (0.114)   {txt}
{txt}subagencyd~n{res}        4.110***{txt}
            {res}      (0.843)   {txt}
{txt}standalone~n{res}        5.843***{txt}
            {res}      (2.287)   {txt}
{txt}okstartsen~n{res}        0.053   {txt}
            {res}      (0.629)   {txt}
{txt}okstartfil~e{res}        0.565   {txt}
            {res}      (1.605)   {txt}
{txt}okcrossover {res}        0.131***{txt}
            {res}      (0.037)   {txt}
{txt}okstartpre~p{res}        1.012   {txt}
            {res}      (0.014)   {txt}
{txt}okstartune~t{res}        0.765   {txt}
            {res}      (0.234)   {txt}
{txt}1.sbagency  {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}2.sbagency  {res}        8.345***{txt}
            {res}      (2.896)   {txt}
{txt}3.sbagency  {res}        4.397***{txt}
            {res}      (1.695)   {txt}
{txt}4.sbagency  {res}        1.243   {txt}
            {res}      (0.439)   {txt}
{txt}5.sbagency  {res}        2.202*  {txt}
            {res}      (0.871)   {txt}
{txt}6.sbagency  {res}        2.993** {txt}
            {res}      (1.134)   {txt}
{txt}7.sbagency  {res}        4.945***{txt}
            {res}      (1.538)   {txt}
{txt}8.sbagency  {res}        7.397***{txt}
            {res}      (2.627)   {txt}
{txt}9.sbagency  {res}        4.204***{txt}
            {res}      (1.403)   {txt}
{txt}12.sbagency {res}        5.292***{txt}
            {res}      (1.827)   {txt}
{txt}13.sbagency {res}        2.752** {txt}
            {res}      (0.904)   {txt}
{txt}14.sbagency {res}        4.253***{txt}
            {res}      (1.354)   {txt}
{txt}15.sbagency {res}        3.766***{txt}
            {res}      (1.014)   {txt}
{txt}16.sbagency {res}        2.425***{txt}
            {res}      (0.335)   {txt}
{txt}17.sbagency {res}        2.892***{txt}
            {res}      (0.507)   {txt}
{txt}18.sbagency {res}        5.758***{txt}
            {res}      (1.878)   {txt}
{txt}19.sbagency {res}        0.541   {txt}
            {res}      (0.194)   {txt}
{txt}20.sbagency {res}        0.278***{txt}
            {res}      (0.093)   {txt}
{txt}21.sbagency {res}        0.682** {txt}
            {res}      (0.093)   {txt}
{txt}22.sbagency {res}        0.094***{txt}
            {res}      (0.056)   {txt}
{txt}23.sbagency {res}        0.549*  {txt}
            {res}      (0.134)   {txt}
{txt}24.sbagency {res}        0.120***{txt}
            {res}      (0.076)   {txt}
{txt}25.sbagency {res}        1.813***{txt}
            {res}      (0.252)   {txt}
{txt}26.sbagency {res}        0.854   {txt}
            {res}      (0.229)   {txt}
{txt}27.sbagency {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}28.sbagency {res}        1.787** {txt}
            {res}      (0.331)   {txt}
{txt}29.sbagency {res}       11.780***{txt}
            {res}      (5.840)   {txt}
{txt}30.sbagency {res}        3.777** {txt}
            {res}      (1.716)   {txt}
{txt}50.sbagency {res}        8.288***{txt}
            {res}      (2.547)   {txt}
{txt}51.sbagency {res}        3.425** {txt}
            {res}      (1.358)   {txt}
{txt}52.sbagency {res}        3.287***{txt}
            {res}      (1.158)   {txt}
{txt}53.sbagency {res}        1.069   {txt}
            {res}      (0.175)   {txt}
{txt}54.sbagency {res}        3.270***{txt}
            {res}      (0.915)   {txt}
{txt}56.sbagency {res}        2.198   {txt}
            {res}      (0.970)   {txt}
{txt}57.sbagency {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}58.sbagency {res}        1.641   {txt}
            {res}      (0.473)   {txt}
{txt}59.sbagency {res}        0.187** {txt}
            {res}      (0.095)   {txt}
{txt}60.sbagency {res}        0.795   {txt}
            {res}      (0.101)   {txt}
{txt}61.sbagency {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}reagan      {res}        1.978   {txt}
            {res}      (2.331)   {txt}
{txt}bush41      {res}        0.794   {txt}
            {res}      (0.306)   {txt}
{txt}clinton     {res}        0.932   {txt}
            {res}      (0.200)   {txt}
{txt}bush43      {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}{hline 28}

{com}. 
. 
. *** COMPUTE Figure G1: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the HAZARD RATIO of APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [MG1A−MG4A] × 1 Horizontal Point Estimates and 95% CIs{c )-}{c )-}. ****
. *** NOTE: IQR = 1.418517 [1.049077 - (-0.36944)] ***
. 
. lincomest 1.soubinaryagency2nom#c.zloyalmedian*1.418517, eform(hr)
{txt}Confidence interval for formula:
{res}1.soubinaryagency2nom#c.zloyalmedian*1.418517

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .5158813{col 26}{space 2} .1230155{col 37}{space 1}   -2.78{col 46}{space 3}0.006{col 54}{space 4} .3232771{col 67}{space 3} .8232368
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelG2Azloyal = r(table)
{txt}
{com}. mat list modelG2Azloyal
{res}
{txt}modelG2Azloyal[9,1]
               (1)
     b {res}  .51588134
{txt}    se {res}  .12301549
{txt}     z {res} -2.7756728
{txt}pvalue {res}  .00550876
{txt}    ll {res}  .32327707
{txt}    ul {res}  .82323675
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          1
{reset}
{com}. 
. 
. 
. ******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. **** MODEL G2B: COX MODEL [INCLUSION OF BOTH AGENCY AND PRESIDENTIAL ADMINISTRATION FIXED EFFECTS: CLUSTER-ADJUSTED STANDARD ERRORS BY AGENCY] ****
. 
. stcox   c.zloyalmedian##i.soubinaryagency2nom  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp  okstartunemployment  i.okstartadyr  i.sbagency reagan bush41 clinton bush43  if okstartadyr!=1,  hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur

{txt}note: 27.sbagency omitted because of collinearity
note: 57.sbagency omitted because of collinearity
note: 61.sbagency omitted because of collinearity
Iteration 0:   log pseudolikelihood = {res}-2550.1086
{txt}Iteration 1:   log pseudolikelihood = {res}-2363.0125
{txt}Iteration 2:   log pseudolikelihood = {res}-2352.4018
{txt}Iteration 3:   log pseudolikelihood = {res}-2352.3032
{txt}Iteration 4:   log pseudolikelihood = {res}-2352.3031
{txt}Refining estimates:
Iteration 0:   log pseudolikelihood = {res}-2352.3031

{txt}Cox regression -- Breslow method for ties

No. of subjects      = {res}         490             {txt}Number of obs    =  {res}       490
{txt}No. of failures      = {res}         490
{txt}Time at risk         = {res}      416913
                                                {txt}Wald chi2({res}40{txt})    =  {res}  45056.82
{txt}Log pseudolikelihood =   {res}-2352.3031             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 100:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}                                _t{col 36}{c |} Haz. Ratio{col 48}   Std. Err.{col 60}      z{col 68}   P>|z|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}zloyalmedian {c |}{col 36}{res}{space 2} 1.290491{col 48}{space 2} .2482096{col 59}{space 1}    1.33{col 68}{space 3}0.185{col 76}{space 4} .8851909{col 89}{space 3} 1.881364
{txt}{space 13}1.soubinaryagency2nom {c |}{col 36}{res}{space 2} 1.317707{col 48}{space 2} .2925634{col 59}{space 1}    1.24{col 68}{space 3}0.214{col 76}{space 4} .8527684{col 89}{space 3} 2.036135
{txt}{space 34} {c |}
soubinaryagency2nom#c.zloyalmedian {c |}
{space 32}1  {c |}{col 36}{res}{space 2} .6187477{col 48}{space 2} .1369861{col 59}{space 1}   -2.17{col 68}{space 3}0.030{col 76}{space 4} .4009258{col 89}{space 3} .9549117
{txt}{space 34} {c |}
{space 21}zpecompmedian {c |}{col 36}{res}{space 2} 1.005961{col 48}{space 2}  .102159{col 59}{space 1}    0.06{col 68}{space 3}0.953{col 76}{space 4} .8244014{col 89}{space 3} 1.227507
{txt}{space 21}zmecompmedian {c |}{col 36}{res}{space 2} 1.041991{col 48}{space 2}  .123936{col 59}{space 1}    0.35{col 68}{space 3}0.729{col 76}{space 4} .8253172{col 89}{space 3}  1.31555
{txt}{space 25}toplevel2 {c |}{col 36}{res}{space 2} .4893591{col 48}{space 2} .0745454{col 59}{space 1}   -4.69{col 68}{space 3}0.000{col 76}{space 4}  .363046{col 89}{space 3} .6596196
{txt}{space 14}presagencyideolalign {c |}{col 36}{res}{space 2} .5853769{col 48}{space 2} .2728619{col 59}{space 1}   -1.15{col 68}{space 3}0.251{col 76}{space 4}  .234782{col 89}{space 3} 1.459508
{txt}{space 12}presagencyideolopposed {c |}{col 36}{res}{space 2} .5126291{col 48}{space 2} .2415949{col 59}{space 1}   -1.42{col 68}{space 3}0.156{col 76}{space 4} .2035373{col 89}{space 3} 1.291108
{txt}{space 19}subagencydesign {c |}{col 36}{res}{space 2}  2.10243{col 48}{space 2} .7419072{col 59}{space 1}    2.11{col 68}{space 3}0.035{col 76}{space 4} 1.052807{col 89}{space 3} 4.198501
{txt}{space 12}standaloneagencydesign {c |}{col 36}{res}{space 2}  1.85799{col 48}{space 2} .9020516{col 59}{space 1}    1.28{col 68}{space 3}0.202{col 76}{space 4} .7174409{col 89}{space 3} 4.811722
{txt}{space 8}okstartsenpolarizationmean {c |}{col 36}{res}{space 2} 7.69e-20{col 48}{space 2} 1.13e-18{col 59}{space 1}   -3.00{col 68}{space 3}0.003{col 76}{space 4} 2.45e-32{col 89}{space 3} 2.41e-07
{txt}{space 11}okstartfilipresdistance {c |}{col 36}{res}{space 2} 8314.621{col 48}{space 2} 30031.98{col 59}{space 1}    2.50{col 68}{space 3}0.012{col 76}{space 4} 7.003998{col 89}{space 3}  9870493
{txt}{space 23}okcrossover {c |}{col 36}{res}{space 2} .1589631{col 48}{space 2} .0449261{col 59}{space 1}   -6.51{col 68}{space 3}0.000{col 76}{space 4} .0913545{col 89}{space 3} .2766068
{txt}{space 20}okstartpresapp {c |}{col 36}{res}{space 2} .9878886{col 48}{space 2} .0095694{col 59}{space 1}   -1.26{col 68}{space 3}0.208{col 76}{space 4} .9693099{col 89}{space 3} 1.006823
{txt}{space 15}okstartunemployment {c |}{col 36}{res}{space 2} 1.483325{col 48}{space 2} .1950086{col 59}{space 1}    3.00{col 68}{space 3}0.003{col 76}{space 4} 1.146387{col 89}{space 3} 1.919293
{txt}{space 34} {c |}
{space 23}okstartadyr {c |}
{space 32}3  {c |}{col 36}{res}{space 2} 2.208975{col 48}{space 2} .4340377{col 59}{space 1}    4.03{col 68}{space 3}0.000{col 76}{space 4} 1.502933{col 89}{space 3} 3.246698
{txt}{space 32}4  {c |}{col 36}{res}{space 2} 2.407227{col 48}{space 2} .9412265{col 59}{space 1}    2.25{col 68}{space 3}0.025{col 76}{space 4} 1.118657{col 89}{space 3} 5.180087
{txt}{space 32}5  {c |}{col 36}{res}{space 2} 2.107345{col 48}{space 2} .8981575{col 59}{space 1}    1.75{col 68}{space 3}0.080{col 76}{space 4} .9140093{col 89}{space 3} 4.858705
{txt}{space 32}6  {c |}{col 36}{res}{space 2} 5.576447{col 48}{space 2} 2.473456{col 59}{space 1}    3.87{col 68}{space 3}0.000{col 76}{space 4}  2.33778{col 89}{space 3} 13.30183
{txt}{space 32}7  {c |}{col 36}{res}{space 2} 8.638394{col 48}{space 2} 4.739829{col 59}{space 1}    3.93{col 68}{space 3}0.000{col 76}{space 4} 2.947029{col 89}{space 3} 25.32104
{txt}{space 32}8  {c |}{col 36}{res}{space 2}  16.6886{col 48}{space 2} 9.786275{col 59}{space 1}    4.80{col 68}{space 3}0.000{col 76}{space 4} 5.287753{col 89}{space 3} 52.67064
{txt}{space 34} {c |}
{space 26}sbagency {c |}
{space 32}2  {c |}{col 36}{res}{space 2} 3.691068{col 48}{space 2} 1.795633{col 59}{space 1}    2.68{col 68}{space 3}0.007{col 76}{space 4} 1.422522{col 89}{space 3} 9.577342
{txt}{space 32}3  {c |}{col 36}{res}{space 2} 2.730502{col 48}{space 2} 1.234601{col 59}{space 1}    2.22{col 68}{space 3}0.026{col 76}{space 4} 1.125564{col 89}{space 3} 6.623915
{txt}{space 32}4  {c |}{col 36}{res}{space 2} 2.720491{col 48}{space 2} .6868291{col 59}{space 1}    3.96{col 68}{space 3}0.000{col 76}{space 4} 1.658626{col 89}{space 3} 4.462169
{txt}{space 32}5  {c |}{col 36}{res}{space 2} .6414302{col 48}{space 2}  .297185{col 59}{space 1}   -0.96{col 68}{space 3}0.338{col 76}{space 4} .2586866{col 89}{space 3} 1.590468
{txt}{space 32}6  {c |}{col 36}{res}{space 2} 3.517102{col 48}{space 2}  1.13835{col 59}{space 1}    3.89{col 68}{space 3}0.000{col 76}{space 4} 1.865022{col 89}{space 3} 6.632633
{txt}{space 32}7  {c |}{col 36}{res}{space 2} 1.960536{col 48}{space 2} .9109975{col 59}{space 1}    1.45{col 68}{space 3}0.147{col 76}{space 4} .7885862{col 89}{space 3} 4.874169
{txt}{space 32}8  {c |}{col 36}{res}{space 2} 2.545543{col 48}{space 2}  1.18756{col 59}{space 1}    2.00{col 68}{space 3}0.045{col 76}{space 4} 1.020172{col 89}{space 3} 6.351662
{txt}{space 32}9  {c |}{col 36}{res}{space 2} 3.448915{col 48}{space 2}  1.45152{col 59}{space 1}    2.94{col 68}{space 3}0.003{col 76}{space 4} 1.511623{col 89}{space 3} 7.869035
{txt}{space 31}11  {c |}{col 36}{res}{space 2} 4.817745{col 48}{space 2} 2.501114{col 59}{space 1}    3.03{col 68}{space 3}0.002{col 76}{space 4} 1.741589{col 89}{space 3} 13.32729
{txt}{space 31}12  {c |}{col 36}{res}{space 2} 1.982419{col 48}{space 2} .6705674{col 59}{space 1}    2.02{col 68}{space 3}0.043{col 76}{space 4} 1.021575{col 89}{space 3} 3.846984
{txt}{space 31}13  {c |}{col 36}{res}{space 2} 3.297234{col 48}{space 2} 1.624506{col 59}{space 1}    2.42{col 68}{space 3}0.015{col 76}{space 4} 1.255375{col 89}{space 3} 8.660164
{txt}{space 31}14  {c |}{col 36}{res}{space 2} 3.555125{col 48}{space 2} 1.801295{col 59}{space 1}    2.50{col 68}{space 3}0.012{col 76}{space 4} 1.316958{col 89}{space 3} 9.597051
{txt}{space 31}15  {c |}{col 36}{res}{space 2} 2.059805{col 48}{space 2} .9548737{col 59}{space 1}    1.56{col 68}{space 3}0.119{col 76}{space 4} .8302911{col 89}{space 3} 5.110009
{txt}{space 31}16  {c |}{col 36}{res}{space 2} .5218658{col 48}{space 2} .1548209{col 59}{space 1}   -2.19{col 68}{space 3}0.028{col 76}{space 4} .2917659{col 89}{space 3} .9334331
{txt}{space 31}17  {c |}{col 36}{res}{space 2} 1.661545{col 48}{space 2} .2922757{col 59}{space 1}    2.89{col 68}{space 3}0.004{col 76}{space 4} 1.177011{col 89}{space 3} 2.345543
{txt}{space 31}18  {c |}{col 36}{res}{space 2}  2.28374{col 48}{space 2} 1.067713{col 59}{space 1}    1.77{col 68}{space 3}0.077{col 76}{space 4} .9134524{col 89}{space 3} 5.709622
{txt}{space 31}19  {c |}{col 36}{res}{space 2} 1.090101{col 48}{space 2}  .200383{col 59}{space 1}    0.47{col 68}{space 3}0.639{col 76}{space 4} .7603233{col 89}{space 3} 1.562914
{txt}{space 31}20  {c |}{col 36}{res}{space 2} .1858075{col 48}{space 2}  .095245{col 59}{space 1}   -3.28{col 68}{space 3}0.001{col 76}{space 4} .0680357{col 89}{space 3} .5074454
{txt}{space 31}21  {c |}{col 36}{res}{space 2} 1.071717{col 48}{space 2} .2437668{col 59}{space 1}    0.30{col 68}{space 3}0.761{col 76}{space 4} .6862316{col 89}{space 3} 1.673747
{txt}{space 31}22  {c |}{col 36}{res}{space 2} 1.105804{col 48}{space 2} .5080668{col 59}{space 1}    0.22{col 68}{space 3}0.827{col 76}{space 4} .4493552{col 89}{space 3} 2.721239
{txt}{space 31}23  {c |}{col 36}{res}{space 2} .9091712{col 48}{space 2} .3926068{col 59}{space 1}   -0.22{col 68}{space 3}0.825{col 76}{space 4} .3900065{col 89}{space 3} 2.119432
{txt}{space 31}24  {c |}{col 36}{res}{space 2} .3441091{col 48}{space 2} .2681948{col 59}{space 1}   -1.37{col 68}{space 3}0.171{col 76}{space 4} .0746929{col 89}{space 3} 1.585306
{txt}{space 31}25  {c |}{col 36}{res}{space 2} 1.662354{col 48}{space 2} .4709349{col 59}{space 1}    1.79{col 68}{space 3}0.073{col 76}{space 4} .9540767{col 89}{space 3} 2.896435
{txt}{space 31}26  {c |}{col 36}{res}{space 2} .9041843{col 48}{space 2} .2003024{col 59}{space 1}   -0.45{col 68}{space 3}0.649{col 76}{space 4} .5857223{col 89}{space 3} 1.395797
{txt}{space 31}27  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 31}28  {c |}{col 36}{res}{space 2} 1.839885{col 48}{space 2} .3692983{col 59}{space 1}    3.04{col 68}{space 3}0.002{col 76}{space 4} 1.241478{col 89}{space 3}  2.72673
{txt}{space 31}29  {c |}{col 36}{res}{space 2} 4.560322{col 48}{space 2} 2.612256{col 59}{space 1}    2.65{col 68}{space 3}0.008{col 76}{space 4} 1.483911{col 89}{space 3} 14.01467
{txt}{space 31}30  {c |}{col 36}{res}{space 2} 2.593728{col 48}{space 2} 1.424958{col 59}{space 1}    1.73{col 68}{space 3}0.083{col 76}{space 4} .8836617{col 89}{space 3}  7.61312
{txt}{space 31}50  {c |}{col 36}{res}{space 2} 2.238632{col 48}{space 2} .7201334{col 59}{space 1}    2.51{col 68}{space 3}0.012{col 76}{space 4} 1.191693{col 89}{space 3}  4.20534
{txt}{space 31}51  {c |}{col 36}{res}{space 2} 2.894551{col 48}{space 2}  .887746{col 59}{space 1}    3.47{col 68}{space 3}0.001{col 76}{space 4} 1.586797{col 89}{space 3} 5.280085
{txt}{space 31}52  {c |}{col 36}{res}{space 2} 1.578298{col 48}{space 2}  .600794{col 59}{space 1}    1.20{col 68}{space 3}0.231{col 76}{space 4} .7484645{col 89}{space 3} 3.328181
{txt}{space 31}53  {c |}{col 36}{res}{space 2} 1.817305{col 48}{space 2} .5605572{col 59}{space 1}    1.94{col 68}{space 3}0.053{col 76}{space 4} .9928193{col 89}{space 3} 3.326484
{txt}{space 31}54  {c |}{col 36}{res}{space 2} 1.870013{col 48}{space 2} .6011001{col 59}{space 1}    1.95{col 68}{space 3}0.051{col 76}{space 4} .9959392{col 89}{space 3} 3.511205
{txt}{space 31}55  {c |}{col 36}{res}{space 2} 1.087877{col 48}{space 2} .4507466{col 59}{space 1}    0.20{col 68}{space 3}0.839{col 76}{space 4} .4829436{col 89}{space 3} 2.450548
{txt}{space 31}56  {c |}{col 36}{res}{space 2} .6116215{col 48}{space 2} .3121197{col 59}{space 1}   -0.96{col 68}{space 3}0.335{col 76}{space 4} .2249581{col 89}{space 3} 1.662891
{txt}{space 31}57  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 31}58  {c |}{col 36}{res}{space 2} 1.575792{col 48}{space 2} .9453903{col 59}{space 1}    0.76{col 68}{space 3}0.448{col 76}{space 4} .4862097{col 89}{space 3} 5.107095
{txt}{space 31}59  {c |}{col 36}{res}{space 2} 1.075408{col 48}{space 2} .6057373{col 59}{space 1}    0.13{col 68}{space 3}0.897{col 76}{space 4} .3565521{col 89}{space 3} 3.243571
{txt}{space 31}60  {c |}{col 36}{res}{space 2} 1.103969{col 48}{space 2} .3463124{col 59}{space 1}    0.32{col 68}{space 3}0.753{col 76}{space 4} .5969491{col 89}{space 3} 2.041629
{txt}{space 31}61  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 34} {c |}
{space 28}reagan {c |}{col 36}{res}{space 2} .0114889{col 48}{space 2} .0171759{col 59}{space 1}   -2.99{col 68}{space 3}0.003{col 76}{space 4} .0006134{col 89}{space 3} .2151931
{txt}{space 28}bush41 {c |}{col 36}{res}{space 2} .1492016{col 48}{space 2}  .158408{col 59}{space 1}   -1.79{col 68}{space 3}0.073{col 76}{space 4}  .018623{col 89}{space 3} 1.195358
{txt}{space 27}clinton {c |}{col 36}{res}{space 2} 1.051102{col 48}{space 2}  .997095{col 59}{space 1}    0.05{col 68}{space 3}0.958{col 76}{space 4} .1637494{col 89}{space 3} 6.746995
{txt}{space 28}bush43 {c |}{col 36}{res}{space 2} .2479971{col 48}{space 2} .3280321{col 59}{space 1}   -1.05{col 68}{space 3}0.292{col 76}{space 4} .0185584{col 89}{space 3} 3.314002
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       490{col 28}-2550.109{col 39}-2352.303{col 50}    40{col 58} 4784.606{col 69} 4952.382
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. estimates store modelG2B
{txt}
{com}. estout modelG2B, cells(b(star fmt(3)) se(par fmt(3))) eform
{res}
{txt}{hline 28}
{txt}                 modelG2B   
{txt}                     b/se   
{txt}{hline 28}
{txt}zloyalmedian{res}        1.290   {txt}
            {res}      (0.248)   {txt}
{txt}0.soubinar~m{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}1.soubinar~m{res}        1.318   {txt}
            {res}      (0.293)   {txt}
{txt}0.soubinar~i{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}1.soubinar~i{res}        0.619*  {txt}
            {res}      (0.137)   {txt}
{txt}zpecompmed~n{res}        1.006   {txt}
            {res}      (0.102)   {txt}
{txt}zmecompmed~n{res}        1.042   {txt}
            {res}      (0.124)   {txt}
{txt}toplevel2   {res}        0.489***{txt}
            {res}      (0.075)   {txt}
{txt}presagency~n{res}        0.585   {txt}
            {res}      (0.273)   {txt}
{txt}presagency~d{res}        0.513   {txt}
            {res}      (0.242)   {txt}
{txt}subagencyd~n{res}        2.102*  {txt}
            {res}      (0.742)   {txt}
{txt}standalone~n{res}        1.858   {txt}
            {res}      (0.902)   {txt}
{txt}okstartsen~n{res}        0.000** {txt}
            {res}      (0.000)   {txt}
{txt}okstartfil~e{res}     8314.621*  {txt}
            {res}  (30031.984)   {txt}
{txt}okcrossover {res}        0.159***{txt}
            {res}      (0.045)   {txt}
{txt}okstartpre~p{res}        0.988   {txt}
            {res}      (0.010)   {txt}
{txt}okstartune~t{res}        1.483** {txt}
            {res}      (0.195)   {txt}
{txt}2.okstarta~r{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}3.okstarta~r{res}        2.209***{txt}
            {res}      (0.434)   {txt}
{txt}4.okstarta~r{res}        2.407*  {txt}
            {res}      (0.941)   {txt}
{txt}5.okstarta~r{res}        2.107   {txt}
            {res}      (0.898)   {txt}
{txt}6.okstarta~r{res}        5.576***{txt}
            {res}      (2.473)   {txt}
{txt}7.okstarta~r{res}        8.638***{txt}
            {res}      (4.740)   {txt}
{txt}8.okstarta~r{res}       16.689***{txt}
            {res}      (9.786)   {txt}
{txt}1.sbagency  {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}2.sbagency  {res}        3.691** {txt}
            {res}      (1.796)   {txt}
{txt}3.sbagency  {res}        2.731*  {txt}
            {res}      (1.235)   {txt}
{txt}4.sbagency  {res}        2.720***{txt}
            {res}      (0.687)   {txt}
{txt}5.sbagency  {res}        0.641   {txt}
            {res}      (0.297)   {txt}
{txt}6.sbagency  {res}        3.517***{txt}
            {res}      (1.138)   {txt}
{txt}7.sbagency  {res}        1.961   {txt}
            {res}      (0.911)   {txt}
{txt}8.sbagency  {res}        2.546*  {txt}
            {res}      (1.188)   {txt}
{txt}9.sbagency  {res}        3.449** {txt}
            {res}      (1.452)   {txt}
{txt}11.sbagency {res}        4.818** {txt}
            {res}      (2.501)   {txt}
{txt}12.sbagency {res}        1.982*  {txt}
            {res}      (0.671)   {txt}
{txt}13.sbagency {res}        3.297*  {txt}
            {res}      (1.625)   {txt}
{txt}14.sbagency {res}        3.555*  {txt}
            {res}      (1.801)   {txt}
{txt}15.sbagency {res}        2.060   {txt}
            {res}      (0.955)   {txt}
{txt}16.sbagency {res}        0.522*  {txt}
            {res}      (0.155)   {txt}
{txt}17.sbagency {res}        1.662** {txt}
            {res}      (0.292)   {txt}
{txt}18.sbagency {res}        2.284   {txt}
            {res}      (1.068)   {txt}
{txt}19.sbagency {res}        1.090   {txt}
            {res}      (0.200)   {txt}
{txt}20.sbagency {res}        0.186** {txt}
            {res}      (0.095)   {txt}
{txt}21.sbagency {res}        1.072   {txt}
            {res}      (0.244)   {txt}
{txt}22.sbagency {res}        1.106   {txt}
            {res}      (0.508)   {txt}
{txt}23.sbagency {res}        0.909   {txt}
            {res}      (0.393)   {txt}
{txt}24.sbagency {res}        0.344   {txt}
            {res}      (0.268)   {txt}
{txt}25.sbagency {res}        1.662   {txt}
            {res}      (0.471)   {txt}
{txt}26.sbagency {res}        0.904   {txt}
            {res}      (0.200)   {txt}
{txt}27.sbagency {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}28.sbagency {res}        1.840** {txt}
            {res}      (0.369)   {txt}
{txt}29.sbagency {res}        4.560** {txt}
            {res}      (2.612)   {txt}
{txt}30.sbagency {res}        2.594   {txt}
            {res}      (1.425)   {txt}
{txt}50.sbagency {res}        2.239*  {txt}
            {res}      (0.720)   {txt}
{txt}51.sbagency {res}        2.895***{txt}
            {res}      (0.888)   {txt}
{txt}52.sbagency {res}        1.578   {txt}
            {res}      (0.601)   {txt}
{txt}53.sbagency {res}        1.817   {txt}
            {res}      (0.561)   {txt}
{txt}54.sbagency {res}        1.870   {txt}
            {res}      (0.601)   {txt}
{txt}55.sbagency {res}        1.088   {txt}
            {res}      (0.451)   {txt}
{txt}56.sbagency {res}        0.612   {txt}
            {res}      (0.312)   {txt}
{txt}57.sbagency {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}58.sbagency {res}        1.576   {txt}
            {res}      (0.945)   {txt}
{txt}59.sbagency {res}        1.075   {txt}
            {res}      (0.606)   {txt}
{txt}60.sbagency {res}        1.104   {txt}
            {res}      (0.346)   {txt}
{txt}61.sbagency {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}reagan      {res}        0.011** {txt}
            {res}      (0.017)   {txt}
{txt}bush41      {res}        0.149   {txt}
            {res}      (0.158)   {txt}
{txt}clinton     {res}        1.051   {txt}
            {res}      (0.997)   {txt}
{txt}bush43      {res}        0.248   {txt}
            {res}      (0.328)   {txt}
{txt}{hline 28}

{com}. 
. 
. *** COMPUTE Figure G1: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the HAZARD RATIO of APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [MG1B−MG4B] × 1 Horizontal Point Estimates and 95% CIs{c )-}{c )-}. ****
. **** NOTE: IQR =  0.7979161  [0.3859205 - (-0.4119956)] *** ***
. 
. lincomest 1.soubinaryagency2nom#c.zloyalmedian*0.7979161, eform(hr)
{txt}Confidence interval for formula:
{res}1.soubinaryagency2nom#c.zloyalmedian*0.7979161

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .6817817{col 26}{space 2} .1204385{col 37}{space 1}   -2.17{col 46}{space 3}0.030{col 54}{space 4} .4822568{col 67}{space 3} .9638564
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelG2Bzloyal = r(table)
{txt}
{com}. mat list modelG2Bzloyal
{res}
{txt}modelG2Bzloyal[9,1]
               (1)
     b {res}   .6817817
{txt}    se {res}  .12043853
{txt}     z {res} -2.1683557
{txt}pvalue {res}  .03013163
{txt}    ll {res}  .48225678
{txt}    ul {res}   .9638564
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          1
{reset}
{com}. 
. 
. 
. 
. **************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. **************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. 
. **** MODEL G3A: WEIBULL MODEL [OMISSION OF BOTH AGENCY AND PRESIDENTIAL ADMINISTRATION FIXED EFFECTS: CLUSTER-ADJUSTED STANDARD ERRORS BY AGENCY] ****
. 
. streg   c.zloyalmedian##i.soubinaryagency2nom  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp okstartunemployment   if okstartadyr==1,   distribution(weibull)  hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur

{txt}Fitting constant-only model:

Iteration 0:   log pseudolikelihood = {res}-409.97648
{txt}Iteration 1:   log pseudolikelihood = {res}-302.11658
{txt}Iteration 2:   log pseudolikelihood = {res} -297.2935
{txt}Iteration 3:   log pseudolikelihood = {res}-297.29108
{txt}Iteration 4:   log pseudolikelihood = {res}-297.29108

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-297.29108}  
Iteration 1:{space 3}log pseudolikelihood = {res:-216.59199}  
Iteration 2:{space 3}log pseudolikelihood = {res:-202.77586}  
Iteration 3:{space 3}log pseudolikelihood = {res: -202.7446}  
Iteration 4:{space 3}log pseudolikelihood = {res: -202.7446}  
{res}
{txt}Weibull PH regression

No. of subjects      = {res}         370             {txt}Number of obs    =  {res}       370
{txt}No. of failures      = {res}         341
{txt}Time at risk         = {res}      433121
                                                {txt}Wald chi2({res}15{txt})    =  {res}    373.38
{txt}Log pseudolikelihood =   {res} -202.7446             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 100:(Std. Err. adjusted for {res:39} clusters in sbagency)}
{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}                                _t{col 36}{c |} Haz. Ratio{col 48}   Std. Err.{col 60}      z{col 68}   P>|z|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}zloyalmedian {c |}{col 36}{res}{space 2} 1.602956{col 48}{space 2} .1676804{col 59}{space 1}    4.51{col 68}{space 3}0.000{col 76}{space 4}  1.30581{col 89}{space 3}  1.96772
{txt}{space 13}1.soubinaryagency2nom {c |}{col 36}{res}{space 2} 1.500432{col 48}{space 2} .2375883{col 59}{space 1}    2.56{col 68}{space 3}0.010{col 76}{space 4} 1.100098{col 89}{space 3}  2.04645
{txt}{space 34} {c |}
soubinaryagency2nom#c.zloyalmedian {c |}
{space 32}1  {c |}{col 36}{res}{space 2} .6263483{col 48}{space 2} .0829452{col 59}{space 1}   -3.53{col 68}{space 3}0.000{col 76}{space 4} .4831635{col 89}{space 3} .8119656
{txt}{space 34} {c |}
{space 21}zpecompmedian {c |}{col 36}{res}{space 2} 1.074697{col 48}{space 2} .0753084{col 59}{space 1}    1.03{col 68}{space 3}0.304{col 76}{space 4} .9367832{col 89}{space 3} 1.232915
{txt}{space 21}zmecompmedian {c |}{col 36}{res}{space 2} .8534254{col 48}{space 2} .0579253{col 59}{space 1}   -2.34{col 68}{space 3}0.020{col 76}{space 4} .7471214{col 89}{space 3} .9748549
{txt}{space 25}toplevel2 {c |}{col 36}{res}{space 2} .5608359{col 48}{space 2} .0783318{col 59}{space 1}   -4.14{col 68}{space 3}0.000{col 76}{space 4} .4265291{col 89}{space 3} .7374337
{txt}{space 14}presagencyideolalign {c |}{col 36}{res}{space 2} 1.063268{col 48}{space 2} .1886941{col 59}{space 1}    0.35{col 68}{space 3}0.730{col 76}{space 4} .7509019{col 89}{space 3} 1.505574
{txt}{space 12}presagencyideolopposed {c |}{col 36}{res}{space 2} 1.183038{col 48}{space 2} .1887653{col 59}{space 1}    1.05{col 68}{space 3}0.292{col 76}{space 4} .8653287{col 89}{space 3} 1.617395
{txt}{space 19}subagencydesign {c |}{col 36}{res}{space 2} 1.312843{col 48}{space 2} .2330921{col 59}{space 1}    1.53{col 68}{space 3}0.125{col 76}{space 4} .9270094{col 89}{space 3} 1.859266
{txt}{space 12}standaloneagencydesign {c |}{col 36}{res}{space 2} .9629049{col 48}{space 2} .1636459{col 59}{space 1}   -0.22{col 68}{space 3}0.824{col 76}{space 4}  .690115{col 89}{space 3} 1.343524
{txt}{space 8}okstartsenpolarizationmean {c |}{col 36}{res}{space 2} .0162929{col 48}{space 2} .0700122{col 59}{space 1}   -0.96{col 68}{space 3}0.338{col 76}{space 4} 3.58e-06{col 89}{space 3} 74.07951
{txt}{space 11}okstartfilipresdistance {c |}{col 36}{res}{space 2} 2.568118{col 48}{space 2} 1.068893{col 59}{space 1}    2.27{col 68}{space 3}0.023{col 76}{space 4} 1.135876{col 89}{space 3} 5.806297
{txt}{space 23}okcrossover {c |}{col 36}{res}{space 2} .2407729{col 48}{space 2} .0476301{col 59}{space 1}   -7.20{col 68}{space 3}0.000{col 76}{space 4} .1633885{col 89}{space 3} .3548082
{txt}{space 20}okstartpresapp {c |}{col 36}{res}{space 2} 1.011653{col 48}{space 2} .0063131{col 59}{space 1}    1.86{col 68}{space 3}0.063{col 76}{space 4} .9993549{col 89}{space 3} 1.024102
{txt}{space 15}okstartunemployment {c |}{col 36}{res}{space 2} .9769874{col 48}{space 2} .0731696{col 59}{space 1}   -0.31{col 68}{space 3}0.756{col 76}{space 4} .8436063{col 89}{space 3} 1.131457
{txt}{space 29}_cons {c |}{col 36}{res}{space 2} 1.44e-08{col 48}{space 2} 4.52e-08{col 59}{space 1}   -5.75{col 68}{space 3}0.000{col 76}{space 4} 3.05e-11{col 89}{space 3} 6.79e-06
{txt}{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 29}/ln_p {c |}{col 36}{res}{space 2} 1.004303{col 48}{space 2} .0432585{col 59}{space 1}   23.22{col 68}{space 3}0.000{col 76}{space 4} .9195183{col 89}{space 3} 1.089088
{txt}{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                                 p {c |}{col 36}{res}{space 2} 2.730005{col 48}{space 2} .1180959{col 76}{space 4} 2.508082{col 89}{space 3} 2.971564
{txt}                               1/p {c |}{col 36}{res}{space 2} .3662997{col 48}{space 2} .0158456{col 76}{space 4} .3365231{col 89}{space 3} .3987111
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline hazard{txt}.{p_end}

{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       370{col 28}-297.2911{col 39}-202.7446{col 50}    17{col 58} 439.4892{col 69} 506.0188
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. estimates store modelG3A
{txt}
{com}. estout modelG3A, cells(b(star fmt(3)) se(par fmt(3))) eform
{res}
{txt}{hline 28}
{txt}                 modelG3A   
{txt}                     b/se   
{txt}{hline 28}
{res}_t                          {txt}
{txt}zloyalmedian{res}        1.603***{txt}
            {res}      (0.168)   {txt}
{txt}0.soubinar~m{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}1.soubinar~m{res}        1.500*  {txt}
            {res}      (0.238)   {txt}
{txt}0.soubinar~i{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}1.soubinar~i{res}        0.626***{txt}
            {res}      (0.083)   {txt}
{txt}zpecompmed~n{res}        1.075   {txt}
            {res}      (0.075)   {txt}
{txt}zmecompmed~n{res}        0.853*  {txt}
            {res}      (0.058)   {txt}
{txt}toplevel2   {res}        0.561***{txt}
            {res}      (0.078)   {txt}
{txt}presagency~n{res}        1.063   {txt}
            {res}      (0.189)   {txt}
{txt}presagency~d{res}        1.183   {txt}
            {res}      (0.189)   {txt}
{txt}subagencyd~n{res}        1.313   {txt}
            {res}      (0.233)   {txt}
{txt}standalone~n{res}        0.963   {txt}
            {res}      (0.164)   {txt}
{txt}okstartsen~n{res}        0.016   {txt}
            {res}      (0.070)   {txt}
{txt}okstartfil~e{res}        2.568*  {txt}
            {res}      (1.069)   {txt}
{txt}okcrossover {res}        0.241***{txt}
            {res}      (0.048)   {txt}
{txt}okstartpre~p{res}        1.012   {txt}
            {res}      (0.006)   {txt}
{txt}okstartune~t{res}        0.977   {txt}
            {res}      (0.073)   {txt}
{txt}_cons       {res}        0.000***{txt}
            {res}      (0.000)   {txt}
{txt}{hline 28}
{res}/                           {txt}
{txt}ln_p        {res}        2.730***{txt}
            {res}      (0.118)   {txt}
{txt}{hline 28}

{com}. 
. 
. *** COMPUTE Figure G1: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the HAZARD RATIO of APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [MG1A−MG4A] × 1 Horizontal Point Estimates and 95% CIs{c )-}{c )-}. ****
. *** NOTE: IQR = 1.418517 [1.049077 - (-0.36944)] ***
. 
. lincomest 1.soubinaryagency2nom#c.zloyalmedian*1.418517, eform(hr)
{txt}Confidence interval for formula:
{res}1.soubinaryagency2nom#c.zloyalmedian*1.418517

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .5149676{col 26}{space 2} .0967364{col 37}{space 1}   -3.53{col 46}{space 3}0.000{col 54}{space 4} .3563544{col 67}{space 3} .7441793
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelG3Azloyal = r(table)
{txt}
{com}. mat list modelG3Azloyal
{res}
{txt}modelG3Azloyal[9,1]
              (1)
     b {res} .51496756
{txt}    se {res} .09673636
{txt}     z {res}  -3.53289
{txt}pvalue {res} .00041104
{txt}    ll {res} .35635442
{txt}    ul {res} .74417931
{txt}    df {res}         .
{txt}  crit {res}  1.959964
{txt} eform {res}         1
{reset}
{com}. 
. 
. 
. **** COMPUTE Figure G2: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the MEDIAN NUMBER OF DAYS OF APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [MG1−MG4] × 1 Horizontal Point Estimates and 95% CIs{c )-}.
. 
. 
. 
. ** Generate 'manual' interaction variable ** 
. generate loyalppdiff = soubinaryagency2nom*zloyalmedian
{txt}
{com}. 
. ** Re-Estimate Model G3A  with 'manual' interaction variable **
. streg   zloyalmedian soubinaryagency2nom loyalppdiff  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp okstartunemployment  if okstartadyr==1, distribution(weibull) hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur

{txt}Fitting constant-only model:

Iteration 0:   log pseudolikelihood = {res}-409.97648
{txt}Iteration 1:   log pseudolikelihood = {res}-302.11658
{txt}Iteration 2:   log pseudolikelihood = {res} -297.2935
{txt}Iteration 3:   log pseudolikelihood = {res}-297.29108
{txt}Iteration 4:   log pseudolikelihood = {res}-297.29108

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-297.29108}  
Iteration 1:{space 3}log pseudolikelihood = {res:-216.59199}  
Iteration 2:{space 3}log pseudolikelihood = {res:-202.77586}  
Iteration 3:{space 3}log pseudolikelihood = {res: -202.7446}  
Iteration 4:{space 3}log pseudolikelihood = {res: -202.7446}  
{res}
{txt}Weibull PH regression

No. of subjects      = {res}         370             {txt}Number of obs    =  {res}       370
{txt}No. of failures      = {res}         341
{txt}Time at risk         = {res}      433121
                                                {txt}Wald chi2({res}15{txt})    =  {res}    373.38
{txt}Log pseudolikelihood =   {res} -202.7446             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 92:(Std. Err. adjusted for {res:39} clusters in sbagency)}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}                        _t{col 28}{c |} Haz. Ratio{col 40}   Std. Err.{col 52}      z{col 60}   P>|z|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}zloyalmedian {c |}{col 28}{res}{space 2} 1.602956{col 40}{space 2} .1676804{col 51}{space 1}    4.51{col 60}{space 3}0.000{col 68}{space 4}  1.30581{col 81}{space 3}  1.96772
{txt}{space 7}soubinaryagency2nom {c |}{col 28}{res}{space 2} 1.500432{col 40}{space 2} .2375883{col 51}{space 1}    2.56{col 60}{space 3}0.010{col 68}{space 4} 1.100098{col 81}{space 3}  2.04645
{txt}{space 15}loyalppdiff {c |}{col 28}{res}{space 2} .6263483{col 40}{space 2} .0829452{col 51}{space 1}   -3.53{col 60}{space 3}0.000{col 68}{space 4} .4831635{col 81}{space 3} .8119656
{txt}{space 13}zpecompmedian {c |}{col 28}{res}{space 2} 1.074697{col 40}{space 2} .0753084{col 51}{space 1}    1.03{col 60}{space 3}0.304{col 68}{space 4} .9367832{col 81}{space 3} 1.232915
{txt}{space 13}zmecompmedian {c |}{col 28}{res}{space 2} .8534254{col 40}{space 2} .0579253{col 51}{space 1}   -2.34{col 60}{space 3}0.020{col 68}{space 4} .7471214{col 81}{space 3} .9748549
{txt}{space 17}toplevel2 {c |}{col 28}{res}{space 2} .5608359{col 40}{space 2} .0783318{col 51}{space 1}   -4.14{col 60}{space 3}0.000{col 68}{space 4} .4265291{col 81}{space 3} .7374337
{txt}{space 6}presagencyideolalign {c |}{col 28}{res}{space 2} 1.063268{col 40}{space 2} .1886941{col 51}{space 1}    0.35{col 60}{space 3}0.730{col 68}{space 4} .7509019{col 81}{space 3} 1.505574
{txt}{space 4}presagencyideolopposed {c |}{col 28}{res}{space 2} 1.183038{col 40}{space 2} .1887653{col 51}{space 1}    1.05{col 60}{space 3}0.292{col 68}{space 4} .8653287{col 81}{space 3} 1.617395
{txt}{space 11}subagencydesign {c |}{col 28}{res}{space 2} 1.312843{col 40}{space 2} .2330921{col 51}{space 1}    1.53{col 60}{space 3}0.125{col 68}{space 4} .9270094{col 81}{space 3} 1.859266
{txt}{space 4}standaloneagencydesign {c |}{col 28}{res}{space 2} .9629049{col 40}{space 2} .1636459{col 51}{space 1}   -0.22{col 60}{space 3}0.824{col 68}{space 4}  .690115{col 81}{space 3} 1.343524
{txt}okstartsenpolarizationmean {c |}{col 28}{res}{space 2} .0162929{col 40}{space 2} .0700122{col 51}{space 1}   -0.96{col 60}{space 3}0.338{col 68}{space 4} 3.58e-06{col 81}{space 3} 74.07951
{txt}{space 3}okstartfilipresdistance {c |}{col 28}{res}{space 2} 2.568118{col 40}{space 2} 1.068893{col 51}{space 1}    2.27{col 60}{space 3}0.023{col 68}{space 4} 1.135876{col 81}{space 3} 5.806297
{txt}{space 15}okcrossover {c |}{col 28}{res}{space 2} .2407729{col 40}{space 2} .0476301{col 51}{space 1}   -7.20{col 60}{space 3}0.000{col 68}{space 4} .1633885{col 81}{space 3} .3548082
{txt}{space 12}okstartpresapp {c |}{col 28}{res}{space 2} 1.011653{col 40}{space 2} .0063131{col 51}{space 1}    1.86{col 60}{space 3}0.063{col 68}{space 4} .9993549{col 81}{space 3} 1.024102
{txt}{space 7}okstartunemployment {c |}{col 28}{res}{space 2} .9769874{col 40}{space 2} .0731696{col 51}{space 1}   -0.31{col 60}{space 3}0.756{col 68}{space 4} .8436063{col 81}{space 3} 1.131457
{txt}{space 21}_cons {c |}{col 28}{res}{space 2} 1.44e-08{col 40}{space 2} 4.52e-08{col 51}{space 1}   -5.75{col 60}{space 3}0.000{col 68}{space 4} 3.05e-11{col 81}{space 3} 6.79e-06
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}/ln_p {c |}{col 28}{res}{space 2} 1.004303{col 40}{space 2} .0432585{col 51}{space 1}   23.22{col 60}{space 3}0.000{col 68}{space 4} .9195183{col 81}{space 3} 1.089088
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                         p {c |}{col 28}{res}{space 2} 2.730005{col 40}{space 2} .1180959{col 68}{space 4} 2.508082{col 81}{space 3} 2.971564
{txt}                       1/p {c |}{col 28}{res}{space 2} .3662997{col 40}{space 2} .0158456{col 68}{space 4} .3365231{col 81}{space 3} .3987111
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline hazard{txt}.{p_end}

{com}. 
. estimate store modelG3Aa
{txt}
{com}. 
. 
. margins, predict(median time) at(loyalppdiff=(-0.36944  1.049077))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       370
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 4}-.36944}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}1.049077}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 1102.731{col 26}{space 2} 29.95877{col 37}{space 1}   36.81{col 46}{space 3}0.000{col 54}{space 4} 1044.012{col 67}{space 3} 1161.449
{txt}{space 10}2  {c |}{col 14}{res}{space 2} 1406.191{col 26}{space 2} 84.13343{col 37}{space 1}   16.71{col 46}{space 3}0.000{col 54}{space 4} 1241.293{col 67}{space 3}  1571.09
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. ** Generate Differential Predicted Median Survival Time of Senate Committee Stage of Confirmation Process -- Based on Interquartile Differential [corresponding to Differential Marginal Hazard Ratio Estimates] **
. margins, predict(median time) at(loyalppdiff=(-0.36944  1.049077))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       370
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 4}-.36944}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}1.049077}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     9.95{col 38}{space 2}   0.0016
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2} 303.4606{col 26}{space 2} 96.18384{col 37}{space 5} 114.9438{col 51}{space 3} 491.9775
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. matrix modelG3Aazloyal = r(table)
{txt}
{com}. mat list modelG3Aazloyal
{res}
{txt}modelG3Aazloyal[9,1]
            r2vs1.
              _at
     b {res} 303.46062
{txt}    se {res} 96.183837
{txt}     z {res} 3.1550064
{txt}pvalue {res} .00160495
{txt}    ll {res} 114.94376
{txt}    ul {res} 491.97748
{txt}    df {res}         .
{txt}  crit {res}  1.959964
{txt} eform {res}         0
{reset}
{com}. 
. 
. 
. 
. estimates restore modelG3Aa
{txt}(results {stata estimates replay modelG3Aa:modelG3Aa} are active now)

{com}. 
. margins, predict(median time) at(loyalppdiff=(-0.6008357 1.862952))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       370
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.6008357}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}1.862952}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 1059.858{col 26}{space 2} 37.46922{col 37}{space 1}   28.29{col 46}{space 3}0.000{col 54}{space 4} 986.4193{col 67}{space 3} 1133.296
{txt}{space 10}2  {c |}{col 14}{res}{space 2} 1616.658{col 26}{space 2} 160.4606{col 37}{space 1}   10.08{col 46}{space 3}0.000{col 54}{space 4} 1302.161{col 67}{space 3} 1931.155
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, predict(median time) at(loyalppdiff=(-0.6008357 1.862952))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       370
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.6008357}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}1.862952}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     8.93{col 38}{space 2}   0.0028
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2} 556.8001{col 26}{space 2} 186.2798{col 37}{space 5} 191.6984{col 51}{space 3} 921.9018
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. matrix modelG3Abzloyal = r(table)
{txt}
{com}. mat list modelG3Abzloyal
{res}
{txt}modelG3Abzloyal[9,1]
            r2vs1.
              _at
     b {res} 556.80013
{txt}    se {res}  186.2798
{txt}     z {res} 2.9890527
{txt}pvalue {res} .00279844
{txt}    ll {res} 191.69844
{txt}    ul {res} 921.90182
{txt}    df {res}         .
{txt}  crit {res}  1.959964
{txt} eform {res}         0
{reset}
{com}. 
. 
. 
. 
. ******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. 
. **** MODEL G3B: WEIBULL MODEL [OMISSION OF BOTH AGENCY AND PRESIDENTIAL ADMINISTRATION FIXED EFFECTS: CLUSTER-ADJUSTED STANDARD ERRORS BY AGENCY] ****
. 
. streg   c.zloyalmedian##i.soubinaryagency2nom  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp okstartunemployment  i.okstartadyr if okstartadyr!=1,   distribution(weibull)  hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur

{txt}Fitting constant-only model:

Iteration 0:   log pseudolikelihood = {res} -586.2933
{txt}Iteration 1:   log pseudolikelihood = {res}-491.77185
{txt}Iteration 2:   log pseudolikelihood = {res} -490.0111
{txt}Iteration 3:   log pseudolikelihood = {res}-490.01096
{txt}Iteration 4:   log pseudolikelihood = {res}-490.01096

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-490.01096}  
Iteration 1:{space 3}log pseudolikelihood = {res:-359.07891}  
Iteration 2:{space 3}log pseudolikelihood = {res:-319.36099}  
Iteration 3:{space 3}log pseudolikelihood = {res:-318.91713}  
Iteration 4:{space 3}log pseudolikelihood = {res:-318.91687}  
Iteration 5:{space 3}log pseudolikelihood = {res:-318.91687}  
{res}
{txt}Weibull PH regression

No. of subjects      = {res}         490             {txt}Number of obs    =  {res}       490
{txt}No. of failures      = {res}         490
{txt}Time at risk         = {res}      416913
                                                {txt}Wald chi2({res}21{txt})    =  {res}   1830.07
{txt}Log pseudolikelihood =   {res}-318.91687             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 100:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}                                _t{col 36}{c |} Haz. Ratio{col 48}   Std. Err.{col 60}      z{col 68}   P>|z|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}zloyalmedian {c |}{col 36}{res}{space 2}  1.28014{col 48}{space 2} .1691643{col 59}{space 1}    1.87{col 68}{space 3}0.062{col 76}{space 4} .9880415{col 89}{space 3} 1.658592
{txt}{space 13}1.soubinaryagency2nom {c |}{col 36}{res}{space 2} 1.019013{col 48}{space 2} .1125407{col 59}{space 1}    0.17{col 68}{space 3}0.865{col 76}{space 4} .8206773{col 89}{space 3} 1.265282
{txt}{space 34} {c |}
soubinaryagency2nom#c.zloyalmedian {c |}
{space 32}1  {c |}{col 36}{res}{space 2} .6806596{col 48}{space 2} .0960642{col 59}{space 1}   -2.73{col 68}{space 3}0.006{col 76}{space 4} .5161745{col 89}{space 3} .8975598
{txt}{space 34} {c |}
{space 21}zpecompmedian {c |}{col 36}{res}{space 2} .9928581{col 48}{space 2} .0908003{col 59}{space 1}   -0.08{col 68}{space 3}0.938{col 76}{space 4} .8299307{col 89}{space 3}  1.18777
{txt}{space 21}zmecompmedian {c |}{col 36}{res}{space 2} 1.101507{col 48}{space 2} .0876451{col 59}{space 1}    1.22{col 68}{space 3}0.224{col 76}{space 4} .9424507{col 89}{space 3} 1.287407
{txt}{space 25}toplevel2 {c |}{col 36}{res}{space 2} .6057649{col 48}{space 2} .0604125{col 59}{space 1}   -5.03{col 68}{space 3}0.000{col 76}{space 4} .4982123{col 89}{space 3} .7365357
{txt}{space 14}presagencyideolalign {c |}{col 36}{res}{space 2} 1.835818{col 48}{space 2} .2443081{col 59}{space 1}    4.56{col 68}{space 3}0.000{col 76}{space 4} 1.414337{col 89}{space 3} 2.382903
{txt}{space 12}presagencyideolopposed {c |}{col 36}{res}{space 2} 1.526149{col 48}{space 2} .2428431{col 59}{space 1}    2.66{col 68}{space 3}0.008{col 76}{space 4} 1.117255{col 89}{space 3} 2.084689
{txt}{space 19}subagencydesign {c |}{col 36}{res}{space 2} 1.006683{col 48}{space 2}  .182715{col 59}{space 1}    0.04{col 68}{space 3}0.971{col 76}{space 4} .7053388{col 89}{space 3} 1.436771
{txt}{space 12}standaloneagencydesign {c |}{col 36}{res}{space 2} .7891581{col 48}{space 2}  .083325{col 59}{space 1}   -2.24{col 68}{space 3}0.025{col 76}{space 4}  .641635{col 89}{space 3} .9705993
{txt}{space 8}okstartsenpolarizationmean {c |}{col 36}{res}{space 2} .0001855{col 48}{space 2} .0005678{col 59}{space 1}   -2.81{col 68}{space 3}0.005{col 76}{space 4} 4.61e-07{col 89}{space 3} .0746825
{txt}{space 11}okstartfilipresdistance {c |}{col 36}{res}{space 2} 2.052135{col 48}{space 2} 1.196169{col 59}{space 1}    1.23{col 68}{space 3}0.217{col 76}{space 4} .6547101{col 89}{space 3} 6.432247
{txt}{space 23}okcrossover {c |}{col 36}{res}{space 2} .1631437{col 48}{space 2} .0366996{col 59}{space 1}   -8.06{col 68}{space 3}0.000{col 76}{space 4} .1049761{col 89}{space 3} .2535421
{txt}{space 20}okstartpresapp {c |}{col 36}{res}{space 2} .9926477{col 48}{space 2} .0045277{col 59}{space 1}   -1.62{col 68}{space 3}0.106{col 76}{space 4} .9838132{col 89}{space 3} 1.001562
{txt}{space 15}okstartunemployment {c |}{col 36}{res}{space 2} .9410199{col 48}{space 2} .0701671{col 59}{space 1}   -0.82{col 68}{space 3}0.415{col 76}{space 4}  .813072{col 89}{space 3} 1.089102
{txt}{space 34} {c |}
{space 23}okstartadyr {c |}
{space 32}3  {c |}{col 36}{res}{space 2} 3.014516{col 48}{space 2} .4640557{col 59}{space 1}    7.17{col 68}{space 3}0.000{col 76}{space 4} 2.229376{col 89}{space 3} 4.076166
{txt}{space 32}4  {c |}{col 36}{res}{space 2} 2.609645{col 48}{space 2} .9014048{col 59}{space 1}    2.78{col 68}{space 3}0.005{col 76}{space 4} 1.326068{col 89}{space 3} 5.135673
{txt}{space 32}5  {c |}{col 36}{res}{space 2} .6540984{col 48}{space 2} .1359262{col 59}{space 1}   -2.04{col 68}{space 3}0.041{col 76}{space 4} .4352687{col 89}{space 3} .9829437
{txt}{space 32}6  {c |}{col 36}{res}{space 2} 1.333179{col 48}{space 2} .2351434{col 59}{space 1}    1.63{col 68}{space 3}0.103{col 76}{space 4}  .943529{col 89}{space 3} 1.883743
{txt}{space 32}7  {c |}{col 36}{res}{space 2}  2.75037{col 48}{space 2} .7914082{col 59}{space 1}    3.52{col 68}{space 3}0.000{col 76}{space 4} 1.564808{col 89}{space 3}  4.83416
{txt}{space 32}8  {c |}{col 36}{res}{space 2} 4.705249{col 48}{space 2} 1.356733{col 59}{space 1}    5.37{col 68}{space 3}0.000{col 76}{space 4} 2.673887{col 89}{space 3} 8.279841
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} .0000174{col 48}{space 2} .0000408{col 59}{space 1}   -4.67{col 68}{space 3}0.000{col 76}{space 4} 1.75e-07{col 89}{space 3} .0017266
{txt}{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 29}/ln_p {c |}{col 36}{res}{space 2} .8971622{col 48}{space 2} .0343697{col 59}{space 1}   26.10{col 68}{space 3}0.000{col 76}{space 4} .8297989{col 89}{space 3} .9645255
{txt}{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                                 p {c |}{col 36}{res}{space 2} 2.452633{col 48}{space 2} .0842962{col 76}{space 4} 2.292858{col 89}{space 3} 2.623543
{txt}                               1/p {c |}{col 36}{res}{space 2} .4077251{col 48}{space 2} .0140134{col 76}{space 4}  .381164{col 89}{space 3}  .436137
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline hazard{txt}.{p_end}

{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       490{col 28} -490.011{col 39}-318.9169{col 50}    23{col 58} 683.8337{col 69} 780.3051
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. estimates store modelG3B
{txt}
{com}. estout modelG3B, cells(b(star fmt(3)) se(par fmt(3))) eform
{res}
{txt}{hline 28}
{txt}                 modelG3B   
{txt}                     b/se   
{txt}{hline 28}
{res}_t                          {txt}
{txt}zloyalmedian{res}        1.280   {txt}
            {res}      (0.169)   {txt}
{txt}0.soubinar~m{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}1.soubinar~m{res}        1.019   {txt}
            {res}      (0.113)   {txt}
{txt}0.soubinar~i{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}1.soubinar~i{res}        0.681** {txt}
            {res}      (0.096)   {txt}
{txt}zpecompmed~n{res}        0.993   {txt}
            {res}      (0.091)   {txt}
{txt}zmecompmed~n{res}        1.102   {txt}
            {res}      (0.088)   {txt}
{txt}toplevel2   {res}        0.606***{txt}
            {res}      (0.060)   {txt}
{txt}presagency~n{res}        1.836***{txt}
            {res}      (0.244)   {txt}
{txt}presagency~d{res}        1.526** {txt}
            {res}      (0.243)   {txt}
{txt}subagencyd~n{res}        1.007   {txt}
            {res}      (0.183)   {txt}
{txt}standalone~n{res}        0.789*  {txt}
            {res}      (0.083)   {txt}
{txt}okstartsen~n{res}        0.000** {txt}
            {res}      (0.001)   {txt}
{txt}okstartfil~e{res}        2.052   {txt}
            {res}      (1.196)   {txt}
{txt}okcrossover {res}        0.163***{txt}
            {res}      (0.037)   {txt}
{txt}okstartpre~p{res}        0.993   {txt}
            {res}      (0.005)   {txt}
{txt}okstartune~t{res}        0.941   {txt}
            {res}      (0.070)   {txt}
{txt}2.okstarta~r{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}3.okstarta~r{res}        3.015***{txt}
            {res}      (0.464)   {txt}
{txt}4.okstarta~r{res}        2.610** {txt}
            {res}      (0.901)   {txt}
{txt}5.okstarta~r{res}        0.654*  {txt}
            {res}      (0.136)   {txt}
{txt}6.okstarta~r{res}        1.333   {txt}
            {res}      (0.235)   {txt}
{txt}7.okstarta~r{res}        2.750***{txt}
            {res}      (0.791)   {txt}
{txt}8.okstarta~r{res}        4.705***{txt}
            {res}      (1.357)   {txt}
{txt}_cons       {res}        0.000***{txt}
            {res}      (0.000)   {txt}
{txt}{hline 28}
{res}/                           {txt}
{txt}ln_p        {res}        2.453***{txt}
            {res}      (0.084)   {txt}
{txt}{hline 28}

{com}. 
. 
. *** COMPUTE Figure G1: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the HAZARD RATIO of APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [MG1B−MG4B] × 1 Horizontal Point Estimates and 95% CIs{c )-}{c )-}. ****
. ** NOTE: IQR =  0.7979161  [0.3859205 - (-0.4119956)] *** 
. 
. lincomest 1.soubinaryagency2nom#c.zloyalmedian*0.7979161, eform(hr)
{txt}Confidence interval for formula:
{res}1.soubinaryagency2nom#c.zloyalmedian*0.7979161

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .7356854{col 26}{space 2} .0828478{col 37}{space 1}   -2.73{col 46}{space 3}0.006{col 54}{space 4} .5899779{col 67}{space 3} .9173785
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelG3Bzloyal = r(table)
{txt}
{com}. mat list modelG3Bzloyal
{res}
{txt}modelG3Bzloyal[9,1]
               (1)
     b {res}   .7356854
{txt}    se {res}  .08284778
{txt}     z {res} -2.7257295
{txt}pvalue {res}  .00641596
{txt}    ll {res}  .58997789
{txt}    ul {res}  .91737845
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          1
{reset}
{com}. 
. 
. 
. **** COMPUTE Figure G2: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the MEDIAN NUMBER OF DAYS OF APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [MG1B−MG4B] × 1 Horizontal Point Estimates and 95% CIs{c )-}.
. 
. 
. 
. ** Generate 'manual' interaction variable ** 
. *generate loyalppdiff = soubinaryagency2nom*zloyalmedian
. 
. ** Re-Estimate Model G3B  with 'manual' interaction variable **
. streg   zloyalmedian soubinaryagency2nom loyalppdiff  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp okstartunemployment  i.okstartadyr if okstartadyr!=1,  distribution(weibull) hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur

{txt}Fitting constant-only model:

Iteration 0:   log pseudolikelihood = {res} -586.2933
{txt}Iteration 1:   log pseudolikelihood = {res}-491.77185
{txt}Iteration 2:   log pseudolikelihood = {res} -490.0111
{txt}Iteration 3:   log pseudolikelihood = {res}-490.01096
{txt}Iteration 4:   log pseudolikelihood = {res}-490.01096

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-490.01096}  
Iteration 1:{space 3}log pseudolikelihood = {res:-359.07891}  
Iteration 2:{space 3}log pseudolikelihood = {res:-319.36099}  
Iteration 3:{space 3}log pseudolikelihood = {res:-318.91713}  
Iteration 4:{space 3}log pseudolikelihood = {res:-318.91687}  
Iteration 5:{space 3}log pseudolikelihood = {res:-318.91687}  
{res}
{txt}Weibull PH regression

No. of subjects      = {res}         490             {txt}Number of obs    =  {res}       490
{txt}No. of failures      = {res}         490
{txt}Time at risk         = {res}      416913
                                                {txt}Wald chi2({res}21{txt})    =  {res}   1830.07
{txt}Log pseudolikelihood =   {res}-318.91687             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 92:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}                        _t{col 28}{c |} Haz. Ratio{col 40}   Std. Err.{col 52}      z{col 60}   P>|z|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}zloyalmedian {c |}{col 28}{res}{space 2}  1.28014{col 40}{space 2} .1691643{col 51}{space 1}    1.87{col 60}{space 3}0.062{col 68}{space 4} .9880415{col 81}{space 3} 1.658592
{txt}{space 7}soubinaryagency2nom {c |}{col 28}{res}{space 2} 1.019013{col 40}{space 2} .1125407{col 51}{space 1}    0.17{col 60}{space 3}0.865{col 68}{space 4} .8206773{col 81}{space 3} 1.265282
{txt}{space 15}loyalppdiff {c |}{col 28}{res}{space 2} .6806596{col 40}{space 2} .0960642{col 51}{space 1}   -2.73{col 60}{space 3}0.006{col 68}{space 4} .5161745{col 81}{space 3} .8975598
{txt}{space 13}zpecompmedian {c |}{col 28}{res}{space 2} .9928581{col 40}{space 2} .0908003{col 51}{space 1}   -0.08{col 60}{space 3}0.938{col 68}{space 4} .8299307{col 81}{space 3}  1.18777
{txt}{space 13}zmecompmedian {c |}{col 28}{res}{space 2} 1.101507{col 40}{space 2} .0876451{col 51}{space 1}    1.22{col 60}{space 3}0.224{col 68}{space 4} .9424507{col 81}{space 3} 1.287407
{txt}{space 17}toplevel2 {c |}{col 28}{res}{space 2} .6057649{col 40}{space 2} .0604125{col 51}{space 1}   -5.03{col 60}{space 3}0.000{col 68}{space 4} .4982123{col 81}{space 3} .7365357
{txt}{space 6}presagencyideolalign {c |}{col 28}{res}{space 2} 1.835818{col 40}{space 2} .2443081{col 51}{space 1}    4.56{col 60}{space 3}0.000{col 68}{space 4} 1.414337{col 81}{space 3} 2.382903
{txt}{space 4}presagencyideolopposed {c |}{col 28}{res}{space 2} 1.526149{col 40}{space 2} .2428431{col 51}{space 1}    2.66{col 60}{space 3}0.008{col 68}{space 4} 1.117255{col 81}{space 3} 2.084689
{txt}{space 11}subagencydesign {c |}{col 28}{res}{space 2} 1.006683{col 40}{space 2}  .182715{col 51}{space 1}    0.04{col 60}{space 3}0.971{col 68}{space 4} .7053388{col 81}{space 3} 1.436771
{txt}{space 4}standaloneagencydesign {c |}{col 28}{res}{space 2} .7891581{col 40}{space 2}  .083325{col 51}{space 1}   -2.24{col 60}{space 3}0.025{col 68}{space 4}  .641635{col 81}{space 3} .9705993
{txt}okstartsenpolarizationmean {c |}{col 28}{res}{space 2} .0001855{col 40}{space 2} .0005678{col 51}{space 1}   -2.81{col 60}{space 3}0.005{col 68}{space 4} 4.61e-07{col 81}{space 3} .0746825
{txt}{space 3}okstartfilipresdistance {c |}{col 28}{res}{space 2} 2.052135{col 40}{space 2} 1.196169{col 51}{space 1}    1.23{col 60}{space 3}0.217{col 68}{space 4} .6547101{col 81}{space 3} 6.432247
{txt}{space 15}okcrossover {c |}{col 28}{res}{space 2} .1631437{col 40}{space 2} .0366996{col 51}{space 1}   -8.06{col 60}{space 3}0.000{col 68}{space 4} .1049761{col 81}{space 3} .2535421
{txt}{space 12}okstartpresapp {c |}{col 28}{res}{space 2} .9926477{col 40}{space 2} .0045277{col 51}{space 1}   -1.62{col 60}{space 3}0.106{col 68}{space 4} .9838132{col 81}{space 3} 1.001562
{txt}{space 7}okstartunemployment {c |}{col 28}{res}{space 2} .9410199{col 40}{space 2} .0701671{col 51}{space 1}   -0.82{col 60}{space 3}0.415{col 68}{space 4}  .813072{col 81}{space 3} 1.089102
{txt}{space 26} {c |}
{space 15}okstartadyr {c |}
{space 24}3  {c |}{col 28}{res}{space 2} 3.014516{col 40}{space 2} .4640557{col 51}{space 1}    7.17{col 60}{space 3}0.000{col 68}{space 4} 2.229376{col 81}{space 3} 4.076166
{txt}{space 24}4  {c |}{col 28}{res}{space 2} 2.609645{col 40}{space 2} .9014048{col 51}{space 1}    2.78{col 60}{space 3}0.005{col 68}{space 4} 1.326068{col 81}{space 3} 5.135673
{txt}{space 24}5  {c |}{col 28}{res}{space 2} .6540984{col 40}{space 2} .1359262{col 51}{space 1}   -2.04{col 60}{space 3}0.041{col 68}{space 4} .4352687{col 81}{space 3} .9829437
{txt}{space 24}6  {c |}{col 28}{res}{space 2} 1.333179{col 40}{space 2} .2351434{col 51}{space 1}    1.63{col 60}{space 3}0.103{col 68}{space 4}  .943529{col 81}{space 3} 1.883743
{txt}{space 24}7  {c |}{col 28}{res}{space 2}  2.75037{col 40}{space 2} .7914082{col 51}{space 1}    3.52{col 60}{space 3}0.000{col 68}{space 4} 1.564808{col 81}{space 3}  4.83416
{txt}{space 24}8  {c |}{col 28}{res}{space 2} 4.705249{col 40}{space 2} 1.356733{col 51}{space 1}    5.37{col 60}{space 3}0.000{col 68}{space 4} 2.673887{col 81}{space 3} 8.279841
{txt}{space 26} {c |}
{space 21}_cons {c |}{col 28}{res}{space 2} .0000174{col 40}{space 2} .0000408{col 51}{space 1}   -4.67{col 60}{space 3}0.000{col 68}{space 4} 1.75e-07{col 81}{space 3} .0017266
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}/ln_p {c |}{col 28}{res}{space 2} .8971622{col 40}{space 2} .0343697{col 51}{space 1}   26.10{col 60}{space 3}0.000{col 68}{space 4} .8297989{col 81}{space 3} .9645255
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                         p {c |}{col 28}{res}{space 2} 2.452633{col 40}{space 2} .0842962{col 68}{space 4} 2.292858{col 81}{space 3} 2.623543
{txt}                       1/p {c |}{col 28}{res}{space 2} .4077251{col 40}{space 2} .0140134{col 68}{space 4}  .381164{col 81}{space 3}  .436137
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline hazard{txt}.{p_end}

{com}. 
. estimate store modelG3Ba
{txt}
{com}. 
. 
. margins, predict(median time) at(loyalppdiff=(-0.4119956  0.3859205))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       490
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.4119956}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}.3859205}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 783.1613{col 26}{space 2} 26.99904{col 37}{space 1}   29.01{col 46}{space 3}0.000{col 54}{space 4} 730.2442{col 67}{space 3} 836.0785
{txt}{space 10}2  {c |}{col 14}{res}{space 2} 887.5732{col 26}{space 2} 23.91992{col 37}{space 1}   37.11{col 46}{space 3}0.000{col 54}{space 4} 840.6911{col 67}{space 3} 934.4554
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. ** Generate Differential Predicted Median Survival Time of Senate Committee Stage of Confirmation Process -- Based on Interquartile Differential [corresponding to Differential Marginal Hazard Ratio Estimates] **
. margins, predict(median time) at(loyalppdiff=(-0.4119956  0.3859205))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       490
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.4119956}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}.3859205}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     7.81{col 38}{space 2}   0.0052
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2} 104.4119{col 26}{space 2} 37.37018{col 37}{space 5} 31.16769{col 51}{space 3} 177.6561
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. matrix modelG3Bazloyal = r(table)
{txt}
{com}. mat list modelG3Bazloyal
{res}
{txt}modelG3Bazloyal[9,1]
            r2vs1.
              _at
     b {res} 104.41189
{txt}    se {res} 37.370179
{txt}     z {res} 2.7939896
{txt}pvalue {res} .00520622
{txt}    ll {res} 31.167686
{txt}    ul {res}  177.6561
{txt}    df {res}         .
{txt}  crit {res}  1.959964
{txt} eform {res}         0
{reset}
{com}. 
. 
. 
. 
. estimates restore modelG3Ba
{txt}(results {stata estimates replay modelG3Ba:modelG3Ba} are active now)

{com}. 
. margins, predict(median time) at(loyalppdiff=(-0.6930394 1.220186))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       490
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.6930394}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}1.220186}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 749.3883{col 26}{space 2} 36.16246{col 37}{space 1}   20.72{col 46}{space 3}0.000{col 54}{space 4} 678.5112{col 67}{space 3} 820.2654
{txt}{space 10}2  {c |}{col 14}{res}{space 2} 1011.657{col 26}{space 2}   68.851{col 37}{space 1}   14.69{col 46}{space 3}0.000{col 54}{space 4} 876.7114{col 67}{space 3} 1146.602
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, predict(median time) at(loyalppdiff=(-0.6930394 1.220186))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       490
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.6930394}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}1.220186}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     7.08{col 38}{space 2}   0.0078
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2} 262.2685{col 26}{space 2} 98.56757{col 37}{space 5} 69.07964{col 51}{space 3} 455.4574
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. matrix modelG3Bbzloyal = r(table)
{txt}
{com}. mat list modelG3Bbzloyal
{res}
{txt}modelG3Bbzloyal[9,1]
            r2vs1.
              _at
     b {res} 262.26852
{txt}    se {res} 98.567571
{txt}     z {res} 2.6607993
{txt}pvalue {res} .00779554
{txt}    ll {res} 69.079636
{txt}    ul {res} 455.45741
{txt}    df {res}         .
{txt}  crit {res}  1.959964
{txt} eform {res}         0
{reset}
{com}. 
. 
. 
. 
. 
. 
. 
. ******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. **** MODEL G4A: WEIBULL MODEL [INCLUSION OF BOTH AGENCY AND PRESIDENTIAL ADMINISTRATION FIXED EFFECTS: CLUSTER-ADJUSTED STANDARD ERRORS BY AGENCY] ****
. 
. streg   c.zloyalmedian##i.soubinaryagency2nom  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp okstartunemployment  i.okstartadyr  i.sbagency reagan bush41 clinton bush43 if okstartadyr==1, distribution(weibull) hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur
{txt}note: 1.okstartadyr omitted because of collinearity
note: 27.sbagency omitted because of collinearity
note: 57.sbagency omitted because of collinearity
note: 61.sbagency omitted because of collinearity
note: bush43 omitted because of collinearity

Fitting constant-only model:

Iteration 0:   log pseudolikelihood = {res}-409.97648
{txt}Iteration 1:   log pseudolikelihood = {res}-302.11658
{txt}Iteration 2:   log pseudolikelihood = {res} -297.2935
{txt}Iteration 3:   log pseudolikelihood = {res}-297.29108
{txt}Iteration 4:   log pseudolikelihood = {res}-297.29108

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-297.29108}  
Iteration 1:{space 3}log pseudolikelihood = {res:-205.84604}  
Iteration 2:{space 3}log pseudolikelihood = {res:-172.25161}  
Iteration 3:{space 3}log pseudolikelihood = {res:-171.12635}  
Iteration 4:{space 3}log pseudolikelihood = {res: -171.0866}  
Iteration 5:{space 3}log pseudolikelihood = {res:-171.08645}  
Iteration 6:{space 3}log pseudolikelihood = {res:-171.08645}  
{res}
{txt}Weibull PH regression

No. of subjects      = {res}         370             {txt}Number of obs    =  {res}       370
{txt}No. of failures      = {res}         341
{txt}Time at risk         = {res}      433121
{col 49}{help j_robustsingular##|_new:Wald chi2(13)}{txt}{col 66}=  {res}         .
{txt}Log pseudolikelihood =   {res}-171.08645             {txt}Prob > chi2      =  {res}         .

{txt}{ralign 100:(Std. Err. adjusted for {res:39} clusters in sbagency)}
{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}                                _t{col 36}{c |} Haz. Ratio{col 48}   Std. Err.{col 60}      z{col 68}   P>|z|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}zloyalmedian {c |}{col 36}{res}{space 2} 1.526544{col 48}{space 2} .1930922{col 59}{space 1}    3.34{col 68}{space 3}0.001{col 76}{space 4} 1.191355{col 89}{space 3}  1.95604
{txt}{space 13}1.soubinaryagency2nom {c |}{col 36}{res}{space 2} 1.174048{col 48}{space 2} .2865928{col 59}{space 1}    0.66{col 68}{space 3}0.511{col 76}{space 4} .7276154{col 89}{space 3} 1.894391
{txt}{space 34} {c |}
soubinaryagency2nom#c.zloyalmedian {c |}
{space 32}1  {c |}{col 36}{res}{space 2} .6651509{col 48}{space 2} .1073874{col 59}{space 1}   -2.53{col 68}{space 3}0.012{col 76}{space 4} .4847247{col 89}{space 3}  .912736
{txt}{space 34} {c |}
{space 21}zpecompmedian {c |}{col 36}{res}{space 2} 1.088955{col 48}{space 2} .1054704{col 59}{space 1}    0.88{col 68}{space 3}0.379{col 76}{space 4} .9006731{col 89}{space 3} 1.316597
{txt}{space 21}zmecompmedian {c |}{col 36}{res}{space 2} .8501761{col 48}{space 2} .0785717{col 59}{space 1}   -1.76{col 68}{space 3}0.079{col 76}{space 4} .7093203{col 89}{space 3} 1.019003
{txt}{space 25}toplevel2 {c |}{col 36}{res}{space 2} .4606812{col 48}{space 2} .0845126{col 59}{space 1}   -4.22{col 68}{space 3}0.000{col 76}{space 4} .3215484{col 89}{space 3} .6600163
{txt}{space 14}presagencyideolalign {c |}{col 36}{res}{space 2} .3800729{col 48}{space 2}  .100269{col 59}{space 1}   -3.67{col 68}{space 3}0.000{col 76}{space 4} .2266245{col 89}{space 3} .6374217
{txt}{space 12}presagencyideolopposed {c |}{col 36}{res}{space 2} .4199032{col 48}{space 2} .1097273{col 59}{space 1}   -3.32{col 68}{space 3}0.001{col 76}{space 4} .2516035{col 89}{space 3} .7007798
{txt}{space 19}subagencydesign {c |}{col 36}{res}{space 2} 3.029673{col 48}{space 2} .5072311{col 59}{space 1}    6.62{col 68}{space 3}0.000{col 76}{space 4} 2.182161{col 89}{space 3} 4.206345
{txt}{space 12}standaloneagencydesign {c |}{col 36}{res}{space 2} 4.263263{col 48}{space 2} 1.374594{col 59}{space 1}    4.50{col 68}{space 3}0.000{col 76}{space 4} 2.266164{col 89}{space 3} 8.020344
{txt}{space 8}okstartsenpolarizationmean {c |}{col 36}{res}{space 2} 95798.06{col 48}{space 2}  1022325{col 59}{space 1}    1.07{col 68}{space 3}0.282{col 76}{space 4}  .000079{col 89}{space 3} 1.16e+14
{txt}{space 11}okstartfilipresdistance {c |}{col 36}{res}{space 2}  .117692{col 48}{space 2} .3041279{col 59}{space 1}   -0.83{col 68}{space 3}0.408{col 76}{space 4} .0007433{col 89}{space 3} 18.63532
{txt}{space 23}okcrossover {c |}{col 36}{res}{space 2} .1876154{col 48}{space 2} .0471194{col 59}{space 1}   -6.66{col 68}{space 3}0.000{col 76}{space 4} .1146806{col 89}{space 3} .3069353
{txt}{space 20}okstartpresapp {c |}{col 36}{res}{space 2} 1.014074{col 48}{space 2} .0127015{col 59}{space 1}    1.12{col 68}{space 3}0.264{col 76}{space 4} .9894829{col 89}{space 3} 1.039277
{txt}{space 15}okstartunemployment {c |}{col 36}{res}{space 2} .8260813{col 48}{space 2} .2337845{col 59}{space 1}   -0.68{col 68}{space 3}0.500{col 76}{space 4}  .474383{col 89}{space 3} 1.438522
{txt}{space 21}1.okstartadyr {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 34} {c |}
{space 26}sbagency {c |}
{space 32}2  {c |}{col 36}{res}{space 2} 5.920727{col 48}{space 2} 1.898317{col 59}{space 1}    5.55{col 68}{space 3}0.000{col 76}{space 4} 3.158354{col 89}{space 3} 11.09914
{txt}{space 32}3  {c |}{col 36}{res}{space 2} 3.246189{col 48}{space 2} 1.194677{col 59}{space 1}    3.20{col 68}{space 3}0.001{col 76}{space 4} 1.578014{col 89}{space 3} 6.677852
{txt}{space 32}4  {c |}{col 36}{res}{space 2} 1.271095{col 48}{space 2} .4632599{col 59}{space 1}    0.66{col 68}{space 3}0.510{col 76}{space 4} .6222309{col 89}{space 3} 2.596598
{txt}{space 32}5  {c |}{col 36}{res}{space 2}  2.02614{col 48}{space 2} .8217011{col 59}{space 1}    1.74{col 68}{space 3}0.082{col 76}{space 4} .9150921{col 89}{space 3} 4.486155
{txt}{space 32}6  {c |}{col 36}{res}{space 2} 2.616121{col 48}{space 2} .9564954{col 59}{space 1}    2.63{col 68}{space 3}0.009{col 76}{space 4} 1.277748{col 89}{space 3} 5.356371
{txt}{space 32}7  {c |}{col 36}{res}{space 2} 3.870342{col 48}{space 2}  1.14047{col 59}{space 1}    4.59{col 68}{space 3}0.000{col 76}{space 4} 2.172333{col 89}{space 3} 6.895602
{txt}{space 32}8  {c |}{col 36}{res}{space 2} 6.457268{col 48}{space 2} 2.145586{col 59}{space 1}    5.61{col 68}{space 3}0.000{col 76}{space 4} 3.366791{col 89}{space 3} 12.38458
{txt}{space 32}9  {c |}{col 36}{res}{space 2} 3.353645{col 48}{space 2} 1.043568{col 59}{space 1}    3.89{col 68}{space 3}0.000{col 76}{space 4} 1.822407{col 89}{space 3} 6.171476
{txt}{space 31}12  {c |}{col 36}{res}{space 2} 3.859965{col 48}{space 2}    1.115{col 59}{space 1}    4.68{col 68}{space 3}0.000{col 76}{space 4} 2.191306{col 89}{space 3} 6.799292
{txt}{space 31}13  {c |}{col 36}{res}{space 2}   2.2774{col 48}{space 2} .6693059{col 59}{space 1}    2.80{col 68}{space 3}0.005{col 76}{space 4} 1.280204{col 89}{space 3} 4.051345
{txt}{space 31}14  {c |}{col 36}{res}{space 2}  3.16428{col 48}{space 2} .9435239{col 59}{space 1}    3.86{col 68}{space 3}0.000{col 76}{space 4}  1.76386{col 89}{space 3} 5.676569
{txt}{space 31}15  {c |}{col 36}{res}{space 2} 3.229388{col 48}{space 2} .7566878{col 59}{space 1}    5.00{col 68}{space 3}0.000{col 76}{space 4}   2.0402{col 89}{space 3}  5.11173
{txt}{space 31}16  {c |}{col 36}{res}{space 2} 2.130328{col 48}{space 2} .3030082{col 59}{space 1}    5.32{col 68}{space 3}0.000{col 76}{space 4} 1.612039{col 89}{space 3} 2.815254
{txt}{space 31}17  {c |}{col 36}{res}{space 2} 2.244257{col 48}{space 2}   .29525{col 59}{space 1}    6.14{col 68}{space 3}0.000{col 76}{space 4} 1.734164{col 89}{space 3}  2.90439
{txt}{space 31}18  {c |}{col 36}{res}{space 2} 4.391668{col 48}{space 2}  1.33413{col 59}{space 1}    4.87{col 68}{space 3}0.000{col 76}{space 4} 2.421284{col 89}{space 3} 7.965502
{txt}{space 31}19  {c |}{col 36}{res}{space 2} .5399059{col 48}{space 2} .1771967{col 59}{space 1}   -1.88{col 68}{space 3}0.060{col 76}{space 4} .2837621{col 89}{space 3} 1.027263
{txt}{space 31}20  {c |}{col 36}{res}{space 2} .2887968{col 48}{space 2} .0851618{col 59}{space 1}   -4.21{col 68}{space 3}0.000{col 76}{space 4} .1620264{col 89}{space 3}  .514753
{txt}{space 31}21  {c |}{col 36}{res}{space 2} .7939326{col 48}{space 2} .1064304{col 59}{space 1}   -1.72{col 68}{space 3}0.085{col 76}{space 4} .6104865{col 89}{space 3} 1.032503
{txt}{space 31}22  {c |}{col 36}{res}{space 2} .1067627{col 48}{space 2} .0574543{col 59}{space 1}   -4.16{col 68}{space 3}0.000{col 76}{space 4} .0371831{col 89}{space 3} .3065444
{txt}{space 31}23  {c |}{col 36}{res}{space 2}  .620922{col 48}{space 2} .1551473{col 59}{space 1}   -1.91{col 68}{space 3}0.056{col 76}{space 4} .3804965{col 89}{space 3} 1.013266
{txt}{space 31}24  {c |}{col 36}{res}{space 2} .1562586{col 48}{space 2} .0708943{col 59}{space 1}   -4.09{col 68}{space 3}0.000{col 76}{space 4} .0642177{col 89}{space 3} .3802184
{txt}{space 31}25  {c |}{col 36}{res}{space 2} 1.949531{col 48}{space 2} .3100185{col 59}{space 1}    4.20{col 68}{space 3}0.000{col 76}{space 4} 1.427481{col 89}{space 3} 2.662503
{txt}{space 31}26  {c |}{col 36}{res}{space 2} .8729228{col 48}{space 2} .2233017{col 59}{space 1}   -0.53{col 68}{space 3}0.595{col 76}{space 4} .5287259{col 89}{space 3} 1.441189
{txt}{space 31}27  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 31}28  {c |}{col 36}{res}{space 2}  1.03018{col 48}{space 2}  .166943{col 59}{space 1}    0.18{col 68}{space 3}0.854{col 76}{space 4} .7498497{col 89}{space 3} 1.415313
{txt}{space 31}29  {c |}{col 36}{res}{space 2} 10.28224{col 48}{space 2} 4.851739{col 59}{space 1}    4.94{col 68}{space 3}0.000{col 76}{space 4} 4.077959{col 89}{space 3} 25.92582
{txt}{space 31}30  {c |}{col 36}{res}{space 2} 4.011186{col 48}{space 2} 1.776571{col 59}{space 1}    3.14{col 68}{space 3}0.002{col 76}{space 4} 1.683729{col 89}{space 3} 9.555941
{txt}{space 31}50  {c |}{col 36}{res}{space 2} 7.933056{col 48}{space 2} 2.366226{col 59}{space 1}    6.94{col 68}{space 3}0.000{col 76}{space 4} 4.421291{col 89}{space 3} 14.23417
{txt}{space 31}51  {c |}{col 36}{res}{space 2} 3.956475{col 48}{space 2} 1.583602{col 59}{space 1}    3.44{col 68}{space 3}0.001{col 76}{space 4} 1.805552{col 89}{space 3} 8.669752
{txt}{space 31}52  {c |}{col 36}{res}{space 2} 4.215921{col 48}{space 2} 1.497085{col 59}{space 1}    4.05{col 68}{space 3}0.000{col 76}{space 4} 2.101979{col 89}{space 3} 8.455836
{txt}{space 31}53  {c |}{col 36}{res}{space 2} .9902975{col 48}{space 2} .1595491{col 59}{space 1}   -0.06{col 68}{space 3}0.952{col 76}{space 4} .7221486{col 89}{space 3} 1.358016
{txt}{space 31}54  {c |}{col 36}{res}{space 2} 3.192881{col 48}{space 2} .7907315{col 59}{space 1}    4.69{col 68}{space 3}0.000{col 76}{space 4} 1.965073{col 89}{space 3} 5.187841
{txt}{space 31}56  {c |}{col 36}{res}{space 2} 2.115886{col 48}{space 2}  .911617{col 59}{space 1}    1.74{col 68}{space 3}0.082{col 76}{space 4} .9094045{col 89}{space 3} 4.922972
{txt}{space 31}57  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 31}58  {c |}{col 36}{res}{space 2}   1.6698{col 48}{space 2} .4653377{col 59}{space 1}    1.84{col 68}{space 3}0.066{col 76}{space 4} .9670582{col 89}{space 3} 2.883209
{txt}{space 31}59  {c |}{col 36}{res}{space 2} .2678526{col 48}{space 2}  .080765{col 59}{space 1}   -4.37{col 68}{space 3}0.000{col 76}{space 4}  .148332{col 89}{space 3} .4836783
{txt}{space 31}60  {c |}{col 36}{res}{space 2} .6702223{col 48}{space 2} .0829602{col 59}{space 1}   -3.23{col 68}{space 3}0.001{col 76}{space 4} .5258442{col 89}{space 3} .8542417
{txt}{space 31}61  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 34} {c |}
{space 28}reagan {c |}{col 36}{res}{space 2} 3.132054{col 48}{space 2}  3.38229{col 59}{space 1}    1.06{col 68}{space 3}0.290{col 76}{space 4} .3772429{col 89}{space 3} 26.00384
{txt}{space 28}bush41 {c |}{col 36}{res}{space 2} 1.105397{col 48}{space 2} .3795884{col 59}{space 1}    0.29{col 68}{space 3}0.770{col 76}{space 4} .5639227{col 89}{space 3} 2.166791
{txt}{space 27}clinton {c |}{col 36}{res}{space 2} .6225199{col 48}{space 2} .1460608{col 59}{space 1}   -2.02{col 68}{space 3}0.043{col 76}{space 4} .3930404{col 89}{space 3} .9859826
{txt}{space 28}bush43 {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 29}_cons {c |}{col 36}{res}{space 2} 1.40e-12{col 48}{space 2} 8.24e-12{col 59}{space 1}   -4.65{col 68}{space 3}0.000{col 76}{space 4} 1.40e-17{col 89}{space 3} 1.40e-07
{txt}{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 29}/ln_p {c |}{col 36}{res}{space 2} 1.123615{col 48}{space 2} .0523381{col 59}{space 1}   21.47{col 68}{space 3}0.000{col 76}{space 4} 1.021034{col 89}{space 3} 1.226196
{txt}{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                                 p {c |}{col 36}{res}{space 2} 3.075954{col 48}{space 2} .1609896{col 76}{space 4} 2.776065{col 89}{space 3}  3.40824
{txt}                               1/p {c |}{col 36}{res}{space 2} .3251024{col 48}{space 2} .0170152{col 76}{space 4} .2934066{col 89}{space 3} .3602222
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline hazard{txt}.{p_end}

{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       370{col 28}-297.2911{col 39}-171.0865{col 50}    15{col 58} 372.1729{col 69} 430.8755
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. estimates store modelG4A
{txt}
{com}. estout modelG4A, cells(b(star fmt(3)) se(par fmt(3))) eform
{res}
{txt}{hline 28}
{txt}                 modelG4A   
{txt}                     b/se   
{txt}{hline 28}
{res}_t                          {txt}
{txt}zloyalmedian{res}        1.527***{txt}
            {res}      (0.193)   {txt}
{txt}0.soubinar~m{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}1.soubinar~m{res}        1.174   {txt}
            {res}      (0.287)   {txt}
{txt}0.soubinar~i{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}1.soubinar~i{res}        0.665*  {txt}
            {res}      (0.107)   {txt}
{txt}zpecompmed~n{res}        1.089   {txt}
            {res}      (0.105)   {txt}
{txt}zmecompmed~n{res}        0.850   {txt}
            {res}      (0.079)   {txt}
{txt}toplevel2   {res}        0.461***{txt}
            {res}      (0.085)   {txt}
{txt}presagency~n{res}        0.380***{txt}
            {res}      (0.100)   {txt}
{txt}presagency~d{res}        0.420***{txt}
            {res}      (0.110)   {txt}
{txt}subagencyd~n{res}        3.030***{txt}
            {res}      (0.507)   {txt}
{txt}standalone~n{res}        4.263***{txt}
            {res}      (1.375)   {txt}
{txt}okstartsen~n{res}    95798.060   {txt}
            {res} (1022324.689)   {txt}
{txt}okstartfil~e{res}        0.118   {txt}
            {res}      (0.304)   {txt}
{txt}okcrossover {res}        0.188***{txt}
            {res}      (0.047)   {txt}
{txt}okstartpre~p{res}        1.014   {txt}
            {res}      (0.013)   {txt}
{txt}okstartune~t{res}        0.826   {txt}
            {res}      (0.234)   {txt}
{txt}1.okstarta~r{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}1.sbagency  {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}2.sbagency  {res}        5.921***{txt}
            {res}      (1.898)   {txt}
{txt}3.sbagency  {res}        3.246** {txt}
            {res}      (1.195)   {txt}
{txt}4.sbagency  {res}        1.271   {txt}
            {res}      (0.463)   {txt}
{txt}5.sbagency  {res}        2.026   {txt}
            {res}      (0.822)   {txt}
{txt}6.sbagency  {res}        2.616** {txt}
            {res}      (0.956)   {txt}
{txt}7.sbagency  {res}        3.870***{txt}
            {res}      (1.140)   {txt}
{txt}8.sbagency  {res}        6.457***{txt}
            {res}      (2.146)   {txt}
{txt}9.sbagency  {res}        3.354***{txt}
            {res}      (1.044)   {txt}
{txt}12.sbagency {res}        3.860***{txt}
            {res}      (1.115)   {txt}
{txt}13.sbagency {res}        2.277** {txt}
            {res}      (0.669)   {txt}
{txt}14.sbagency {res}        3.164***{txt}
            {res}      (0.944)   {txt}
{txt}15.sbagency {res}        3.229***{txt}
            {res}      (0.757)   {txt}
{txt}16.sbagency {res}        2.130***{txt}
            {res}      (0.303)   {txt}
{txt}17.sbagency {res}        2.244***{txt}
            {res}      (0.295)   {txt}
{txt}18.sbagency {res}        4.392***{txt}
            {res}      (1.334)   {txt}
{txt}19.sbagency {res}        0.540   {txt}
            {res}      (0.177)   {txt}
{txt}20.sbagency {res}        0.289***{txt}
            {res}      (0.085)   {txt}
{txt}21.sbagency {res}        0.794   {txt}
            {res}      (0.106)   {txt}
{txt}22.sbagency {res}        0.107***{txt}
            {res}      (0.057)   {txt}
{txt}23.sbagency {res}        0.621   {txt}
            {res}      (0.155)   {txt}
{txt}24.sbagency {res}        0.156***{txt}
            {res}      (0.071)   {txt}
{txt}25.sbagency {res}        1.950***{txt}
            {res}      (0.310)   {txt}
{txt}26.sbagency {res}        0.873   {txt}
            {res}      (0.223)   {txt}
{txt}27.sbagency {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}28.sbagency {res}        1.030   {txt}
            {res}      (0.167)   {txt}
{txt}29.sbagency {res}       10.282***{txt}
            {res}      (4.852)   {txt}
{txt}30.sbagency {res}        4.011** {txt}
            {res}      (1.777)   {txt}
{txt}50.sbagency {res}        7.933***{txt}
            {res}      (2.366)   {txt}
{txt}51.sbagency {res}        3.956***{txt}
            {res}      (1.584)   {txt}
{txt}52.sbagency {res}        4.216***{txt}
            {res}      (1.497)   {txt}
{txt}53.sbagency {res}        0.990   {txt}
            {res}      (0.160)   {txt}
{txt}54.sbagency {res}        3.193***{txt}
            {res}      (0.791)   {txt}
{txt}56.sbagency {res}        2.116   {txt}
            {res}      (0.912)   {txt}
{txt}57.sbagency {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}58.sbagency {res}        1.670   {txt}
            {res}      (0.465)   {txt}
{txt}59.sbagency {res}        0.268***{txt}
            {res}      (0.081)   {txt}
{txt}60.sbagency {res}        0.670** {txt}
            {res}      (0.083)   {txt}
{txt}61.sbagency {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}reagan      {res}        3.132   {txt}
            {res}      (3.382)   {txt}
{txt}bush41      {res}        1.105   {txt}
            {res}      (0.380)   {txt}
{txt}clinton     {res}        0.623*  {txt}
            {res}      (0.146)   {txt}
{txt}bush43      {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}_cons       {res}        0.000***{txt}
            {res}      (0.000)   {txt}
{txt}{hline 28}
{res}/                           {txt}
{txt}ln_p        {res}        3.076***{txt}
            {res}      (0.161)   {txt}
{txt}{hline 28}

{com}. 
. 
. 
. *** COMPUTE Figure G1: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the HAZARD RATIO of APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [MG1A−MG4A] × 1 Horizontal Point Estimates and 95% CIs{c )-}{c )-}. ****
. ** NOTE: NOTE: IQR = 1.418517 [1.049077 - (-0.36944)] 
. 
. lincomest 1.soubinaryagency2nom#c.zloyalmedian*0.8404716, eform(hr)
{txt}Confidence interval for formula:
{res}1.soubinaryagency2nom#c.zloyalmedian*0.8404716

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .7098547{col 26}{space 2} .0963221{col 37}{space 1}   -2.53{col 46}{space 3}0.012{col 54}{space 4} .5440861{col 67}{space 3} .9261285
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelG4Azloyal = r(table)
{txt}
{com}. mat list modelG4Azloyal
{res}
{txt}modelG4Azloyal[9,1]
               (1)
     b {res}  .70985465
{txt}    se {res}  .09632207
{txt}     z {res} -2.5255238
{txt}pvalue {res}   .0115526
{txt}    ll {res}   .5440861
{txt}    ul {res}  .92612847
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          1
{reset}
{com}. 
. 
. 
. 
. **** COMPUTE Figure G2: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the MEDIAN NUMBER OF DAYS OF APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [MG1A−MG4A] × 1 Horizontal Point Estimates and 95% CIs{c )-}.
. 
. ** Re-Estimate Model G4A  with 'manual' interaction variable **
. streg   zloyalmedian soubinaryagency2nom loyalppdiff  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp okstartunemployment   i.sbagency reagan bush41 clinton bush43 if okstartadyr==1, distribution(weibull) hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur
{txt}note: 27.sbagency omitted because of collinearity
note: 57.sbagency omitted because of collinearity
note: 61.sbagency omitted because of collinearity
note: bush43 omitted because of collinearity

Fitting constant-only model:

Iteration 0:   log pseudolikelihood = {res}-409.97648
{txt}Iteration 1:   log pseudolikelihood = {res}-302.11658
{txt}Iteration 2:   log pseudolikelihood = {res} -297.2935
{txt}Iteration 3:   log pseudolikelihood = {res}-297.29108
{txt}Iteration 4:   log pseudolikelihood = {res}-297.29108

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-297.29108}  
Iteration 1:{space 3}log pseudolikelihood = {res:-205.84604}  
Iteration 2:{space 3}log pseudolikelihood = {res:-172.25161}  
Iteration 3:{space 3}log pseudolikelihood = {res:-171.12635}  
Iteration 4:{space 3}log pseudolikelihood = {res: -171.0866}  
Iteration 5:{space 3}log pseudolikelihood = {res:-171.08645}  
Iteration 6:{space 3}log pseudolikelihood = {res:-171.08645}  
{res}
{txt}Weibull PH regression

No. of subjects      = {res}         370             {txt}Number of obs    =  {res}       370
{txt}No. of failures      = {res}         341
{txt}Time at risk         = {res}      433121
{col 49}{help j_robustsingular##|_new:Wald chi2(13)}{txt}{col 66}=  {res}         .
{txt}Log pseudolikelihood =   {res}-171.08645             {txt}Prob > chi2      =  {res}         .

{txt}{ralign 92:(Std. Err. adjusted for {res:39} clusters in sbagency)}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}                        _t{col 28}{c |} Haz. Ratio{col 40}   Std. Err.{col 52}      z{col 60}   P>|z|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}zloyalmedian {c |}{col 28}{res}{space 2} 1.526544{col 40}{space 2} .1930922{col 51}{space 1}    3.34{col 60}{space 3}0.001{col 68}{space 4} 1.191355{col 81}{space 3}  1.95604
{txt}{space 7}soubinaryagency2nom {c |}{col 28}{res}{space 2} 1.174048{col 40}{space 2} .2865928{col 51}{space 1}    0.66{col 60}{space 3}0.511{col 68}{space 4} .7276154{col 81}{space 3} 1.894391
{txt}{space 15}loyalppdiff {c |}{col 28}{res}{space 2} .6651509{col 40}{space 2} .1073874{col 51}{space 1}   -2.53{col 60}{space 3}0.012{col 68}{space 4} .4847247{col 81}{space 3}  .912736
{txt}{space 13}zpecompmedian {c |}{col 28}{res}{space 2} 1.088955{col 40}{space 2} .1054704{col 51}{space 1}    0.88{col 60}{space 3}0.379{col 68}{space 4} .9006731{col 81}{space 3} 1.316597
{txt}{space 13}zmecompmedian {c |}{col 28}{res}{space 2} .8501761{col 40}{space 2} .0785717{col 51}{space 1}   -1.76{col 60}{space 3}0.079{col 68}{space 4} .7093203{col 81}{space 3} 1.019003
{txt}{space 17}toplevel2 {c |}{col 28}{res}{space 2} .4606812{col 40}{space 2} .0845126{col 51}{space 1}   -4.22{col 60}{space 3}0.000{col 68}{space 4} .3215484{col 81}{space 3} .6600163
{txt}{space 6}presagencyideolalign {c |}{col 28}{res}{space 2} .3800729{col 40}{space 2}  .100269{col 51}{space 1}   -3.67{col 60}{space 3}0.000{col 68}{space 4} .2266245{col 81}{space 3} .6374217
{txt}{space 4}presagencyideolopposed {c |}{col 28}{res}{space 2} .4199032{col 40}{space 2} .1097273{col 51}{space 1}   -3.32{col 60}{space 3}0.001{col 68}{space 4} .2516035{col 81}{space 3} .7007798
{txt}{space 11}subagencydesign {c |}{col 28}{res}{space 2} 3.029673{col 40}{space 2} .5072311{col 51}{space 1}    6.62{col 60}{space 3}0.000{col 68}{space 4} 2.182161{col 81}{space 3} 4.206345
{txt}{space 4}standaloneagencydesign {c |}{col 28}{res}{space 2} 4.263263{col 40}{space 2} 1.374594{col 51}{space 1}    4.50{col 60}{space 3}0.000{col 68}{space 4} 2.266164{col 81}{space 3} 8.020344
{txt}okstartsenpolarizationmean {c |}{col 28}{res}{space 2} 95798.06{col 40}{space 2}  1022325{col 51}{space 1}    1.07{col 60}{space 3}0.282{col 68}{space 4}  .000079{col 81}{space 3} 1.16e+14
{txt}{space 3}okstartfilipresdistance {c |}{col 28}{res}{space 2}  .117692{col 40}{space 2} .3041279{col 51}{space 1}   -0.83{col 60}{space 3}0.408{col 68}{space 4} .0007433{col 81}{space 3} 18.63532
{txt}{space 15}okcrossover {c |}{col 28}{res}{space 2} .1876154{col 40}{space 2} .0471194{col 51}{space 1}   -6.66{col 60}{space 3}0.000{col 68}{space 4} .1146806{col 81}{space 3} .3069353
{txt}{space 12}okstartpresapp {c |}{col 28}{res}{space 2} 1.014074{col 40}{space 2} .0127015{col 51}{space 1}    1.12{col 60}{space 3}0.264{col 68}{space 4} .9894829{col 81}{space 3} 1.039277
{txt}{space 7}okstartunemployment {c |}{col 28}{res}{space 2} .8260813{col 40}{space 2} .2337845{col 51}{space 1}   -0.68{col 60}{space 3}0.500{col 68}{space 4}  .474383{col 81}{space 3} 1.438522
{txt}{space 26} {c |}
{space 18}sbagency {c |}
{space 24}2  {c |}{col 28}{res}{space 2} 5.920727{col 40}{space 2} 1.898317{col 51}{space 1}    5.55{col 60}{space 3}0.000{col 68}{space 4} 3.158354{col 81}{space 3} 11.09914
{txt}{space 24}3  {c |}{col 28}{res}{space 2} 3.246189{col 40}{space 2} 1.194677{col 51}{space 1}    3.20{col 60}{space 3}0.001{col 68}{space 4} 1.578014{col 81}{space 3} 6.677852
{txt}{space 24}4  {c |}{col 28}{res}{space 2} 1.271095{col 40}{space 2} .4632599{col 51}{space 1}    0.66{col 60}{space 3}0.510{col 68}{space 4} .6222309{col 81}{space 3} 2.596598
{txt}{space 24}5  {c |}{col 28}{res}{space 2}  2.02614{col 40}{space 2} .8217011{col 51}{space 1}    1.74{col 60}{space 3}0.082{col 68}{space 4} .9150921{col 81}{space 3} 4.486155
{txt}{space 24}6  {c |}{col 28}{res}{space 2} 2.616121{col 40}{space 2} .9564954{col 51}{space 1}    2.63{col 60}{space 3}0.009{col 68}{space 4} 1.277748{col 81}{space 3} 5.356371
{txt}{space 24}7  {c |}{col 28}{res}{space 2} 3.870342{col 40}{space 2}  1.14047{col 51}{space 1}    4.59{col 60}{space 3}0.000{col 68}{space 4} 2.172333{col 81}{space 3} 6.895602
{txt}{space 24}8  {c |}{col 28}{res}{space 2} 6.457268{col 40}{space 2} 2.145586{col 51}{space 1}    5.61{col 60}{space 3}0.000{col 68}{space 4} 3.366791{col 81}{space 3} 12.38458
{txt}{space 24}9  {c |}{col 28}{res}{space 2} 3.353645{col 40}{space 2} 1.043568{col 51}{space 1}    3.89{col 60}{space 3}0.000{col 68}{space 4} 1.822407{col 81}{space 3} 6.171476
{txt}{space 23}12  {c |}{col 28}{res}{space 2} 3.859965{col 40}{space 2}    1.115{col 51}{space 1}    4.68{col 60}{space 3}0.000{col 68}{space 4} 2.191306{col 81}{space 3} 6.799292
{txt}{space 23}13  {c |}{col 28}{res}{space 2}   2.2774{col 40}{space 2} .6693059{col 51}{space 1}    2.80{col 60}{space 3}0.005{col 68}{space 4} 1.280204{col 81}{space 3} 4.051345
{txt}{space 23}14  {c |}{col 28}{res}{space 2}  3.16428{col 40}{space 2} .9435239{col 51}{space 1}    3.86{col 60}{space 3}0.000{col 68}{space 4}  1.76386{col 81}{space 3} 5.676569
{txt}{space 23}15  {c |}{col 28}{res}{space 2} 3.229388{col 40}{space 2} .7566878{col 51}{space 1}    5.00{col 60}{space 3}0.000{col 68}{space 4}   2.0402{col 81}{space 3}  5.11173
{txt}{space 23}16  {c |}{col 28}{res}{space 2} 2.130328{col 40}{space 2} .3030082{col 51}{space 1}    5.32{col 60}{space 3}0.000{col 68}{space 4} 1.612039{col 81}{space 3} 2.815254
{txt}{space 23}17  {c |}{col 28}{res}{space 2} 2.244257{col 40}{space 2}   .29525{col 51}{space 1}    6.14{col 60}{space 3}0.000{col 68}{space 4} 1.734164{col 81}{space 3}  2.90439
{txt}{space 23}18  {c |}{col 28}{res}{space 2} 4.391668{col 40}{space 2}  1.33413{col 51}{space 1}    4.87{col 60}{space 3}0.000{col 68}{space 4} 2.421284{col 81}{space 3} 7.965502
{txt}{space 23}19  {c |}{col 28}{res}{space 2} .5399059{col 40}{space 2} .1771967{col 51}{space 1}   -1.88{col 60}{space 3}0.060{col 68}{space 4} .2837621{col 81}{space 3} 1.027263
{txt}{space 23}20  {c |}{col 28}{res}{space 2} .2887968{col 40}{space 2} .0851618{col 51}{space 1}   -4.21{col 60}{space 3}0.000{col 68}{space 4} .1620264{col 81}{space 3}  .514753
{txt}{space 23}21  {c |}{col 28}{res}{space 2} .7939326{col 40}{space 2} .1064304{col 51}{space 1}   -1.72{col 60}{space 3}0.085{col 68}{space 4} .6104865{col 81}{space 3} 1.032503
{txt}{space 23}22  {c |}{col 28}{res}{space 2} .1067627{col 40}{space 2} .0574543{col 51}{space 1}   -4.16{col 60}{space 3}0.000{col 68}{space 4} .0371831{col 81}{space 3} .3065444
{txt}{space 23}23  {c |}{col 28}{res}{space 2}  .620922{col 40}{space 2} .1551473{col 51}{space 1}   -1.91{col 60}{space 3}0.056{col 68}{space 4} .3804965{col 81}{space 3} 1.013266
{txt}{space 23}24  {c |}{col 28}{res}{space 2} .1562586{col 40}{space 2} .0708943{col 51}{space 1}   -4.09{col 60}{space 3}0.000{col 68}{space 4} .0642177{col 81}{space 3} .3802184
{txt}{space 23}25  {c |}{col 28}{res}{space 2} 1.949531{col 40}{space 2} .3100185{col 51}{space 1}    4.20{col 60}{space 3}0.000{col 68}{space 4} 1.427481{col 81}{space 3} 2.662503
{txt}{space 23}26  {c |}{col 28}{res}{space 2} .8729228{col 40}{space 2} .2233017{col 51}{space 1}   -0.53{col 60}{space 3}0.595{col 68}{space 4} .5287259{col 81}{space 3} 1.441189
{txt}{space 23}27  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (omitted)
{space 23}28  {c |}{col 28}{res}{space 2}  1.03018{col 40}{space 2}  .166943{col 51}{space 1}    0.18{col 60}{space 3}0.854{col 68}{space 4} .7498497{col 81}{space 3} 1.415313
{txt}{space 23}29  {c |}{col 28}{res}{space 2} 10.28224{col 40}{space 2} 4.851739{col 51}{space 1}    4.94{col 60}{space 3}0.000{col 68}{space 4} 4.077959{col 81}{space 3} 25.92582
{txt}{space 23}30  {c |}{col 28}{res}{space 2} 4.011186{col 40}{space 2} 1.776571{col 51}{space 1}    3.14{col 60}{space 3}0.002{col 68}{space 4} 1.683729{col 81}{space 3} 9.555941
{txt}{space 23}50  {c |}{col 28}{res}{space 2} 7.933056{col 40}{space 2} 2.366226{col 51}{space 1}    6.94{col 60}{space 3}0.000{col 68}{space 4} 4.421291{col 81}{space 3} 14.23417
{txt}{space 23}51  {c |}{col 28}{res}{space 2} 3.956475{col 40}{space 2} 1.583602{col 51}{space 1}    3.44{col 60}{space 3}0.001{col 68}{space 4} 1.805552{col 81}{space 3} 8.669752
{txt}{space 23}52  {c |}{col 28}{res}{space 2} 4.215921{col 40}{space 2} 1.497085{col 51}{space 1}    4.05{col 60}{space 3}0.000{col 68}{space 4} 2.101979{col 81}{space 3} 8.455836
{txt}{space 23}53  {c |}{col 28}{res}{space 2} .9902975{col 40}{space 2} .1595491{col 51}{space 1}   -0.06{col 60}{space 3}0.952{col 68}{space 4} .7221486{col 81}{space 3} 1.358016
{txt}{space 23}54  {c |}{col 28}{res}{space 2} 3.192881{col 40}{space 2} .7907315{col 51}{space 1}    4.69{col 60}{space 3}0.000{col 68}{space 4} 1.965073{col 81}{space 3} 5.187841
{txt}{space 23}56  {c |}{col 28}{res}{space 2} 2.115886{col 40}{space 2}  .911617{col 51}{space 1}    1.74{col 60}{space 3}0.082{col 68}{space 4} .9094045{col 81}{space 3} 4.922972
{txt}{space 23}57  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (omitted)
{space 23}58  {c |}{col 28}{res}{space 2}   1.6698{col 40}{space 2} .4653377{col 51}{space 1}    1.84{col 60}{space 3}0.066{col 68}{space 4} .9670582{col 81}{space 3} 2.883209
{txt}{space 23}59  {c |}{col 28}{res}{space 2} .2678526{col 40}{space 2}  .080765{col 51}{space 1}   -4.37{col 60}{space 3}0.000{col 68}{space 4}  .148332{col 81}{space 3} .4836783
{txt}{space 23}60  {c |}{col 28}{res}{space 2} .6702223{col 40}{space 2} .0829602{col 51}{space 1}   -3.23{col 60}{space 3}0.001{col 68}{space 4} .5258442{col 81}{space 3} .8542417
{txt}{space 23}61  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (omitted)
{space 26} {c |}
{space 20}reagan {c |}{col 28}{res}{space 2} 3.132054{col 40}{space 2}  3.38229{col 51}{space 1}    1.06{col 60}{space 3}0.290{col 68}{space 4} .3772429{col 81}{space 3} 26.00384
{txt}{space 20}bush41 {c |}{col 28}{res}{space 2} 1.105397{col 40}{space 2} .3795884{col 51}{space 1}    0.29{col 60}{space 3}0.770{col 68}{space 4} .5639227{col 81}{space 3} 2.166791
{txt}{space 19}clinton {c |}{col 28}{res}{space 2} .6225199{col 40}{space 2} .1460608{col 51}{space 1}   -2.02{col 60}{space 3}0.043{col 68}{space 4} .3930404{col 81}{space 3} .9859826
{txt}{space 20}bush43 {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (omitted)
{space 21}_cons {c |}{col 28}{res}{space 2} 1.40e-12{col 40}{space 2} 8.24e-12{col 51}{space 1}   -4.65{col 60}{space 3}0.000{col 68}{space 4} 1.40e-17{col 81}{space 3} 1.40e-07
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}/ln_p {c |}{col 28}{res}{space 2} 1.123615{col 40}{space 2} .0523381{col 51}{space 1}   21.47{col 60}{space 3}0.000{col 68}{space 4} 1.021034{col 81}{space 3} 1.226196
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                         p {c |}{col 28}{res}{space 2} 3.075954{col 40}{space 2} .1609896{col 68}{space 4} 2.776065{col 81}{space 3}  3.40824
{txt}                       1/p {c |}{col 28}{res}{space 2} .3251024{col 40}{space 2} .0170152{col 68}{space 4} .2934066{col 81}{space 3} .3602222
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline hazard{txt}.{p_end}

{com}. 
. estimates store modelG4Aa
{txt}
{com}. 
. margins, predict(median time) at(loyalppdiff=(-0.36944  1.049077))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       370
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 4}-.36944}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}1.049077}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 1137.798{col 26}{space 2}  41.3274{col 37}{space 1}   27.53{col 46}{space 3}0.000{col 54}{space 4} 1056.797{col 67}{space 3} 1218.798
{txt}{space 10}2  {c |}{col 14}{res}{space 2} 1373.181{col 26}{space 2} 64.31007{col 37}{space 1}   21.35{col 46}{space 3}0.000{col 54}{space 4} 1247.135{col 67}{space 3} 1499.226
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. ** Generate Differential Predicted Median Survival Time of Senate Committee Stage of Confirmation Process -- Based on Interquartile Differential [corresponding to Differential Marginal Hazard Ratio Estimates] **
. margins, predict(median time) at(loyalppdiff=(-0.36944 1.049077))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       370
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 4}-.36944}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}1.049077}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     5.60{col 38}{space 2}   0.0180
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2} 235.3832{col 26}{space 2} 99.49891{col 37}{space 5} 40.36895{col 51}{space 3} 430.3975
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. matrix modelG4Aazloyal = r(table)
{txt}
{com}. mat list modelG4Aazloyal
{res}
{txt}modelG4Aazloyal[9,1]
            r2vs1.
              _at
     b {res} 235.38323
{txt}    se {res}  99.49891
{txt}     z {res} 2.3656865
{txt}pvalue {res} .01799668
{txt}    ll {res} 40.368946
{txt}    ul {res} 430.39751
{txt}    df {res}         .
{txt}  crit {res}  1.959964
{txt} eform {res}         0
{reset}
{com}. 
. 
. 
. estimates restore modelG4Aa
{txt}(results {stata estimates replay modelG4Aa:modelG4Aa} are active now)

{com}. 
. margins, predict(median time) at(loyalppdiff=(-0.6008357 1.862952))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       370
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.6008357}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}1.862952}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 1103.428{col 26}{space 2} 53.32867{col 37}{space 1}   20.69{col 46}{space 3}0.000{col 54}{space 4} 998.9052{col 67}{space 3}  1207.95
{txt}{space 10}2  {c |}{col 14}{res}{space 2} 1529.614{col 26}{space 2} 138.6027{col 37}{space 1}   11.04{col 46}{space 3}0.000{col 54}{space 4} 1257.958{col 67}{space 3}  1801.27
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, predict(median time) at(loyalppdiff=(-0.6008357 1.862952))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       370
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.6008357}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}1.862952}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     5.14{col 38}{space 2}   0.0234
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2} 426.1863{col 26}{space 2} 188.0383{col 37}{space 5} 57.63792{col 51}{space 3} 794.7346
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. matrix modelG4Abzloyal = r(table)
{txt}
{com}. mat list modelG4Abzloyal
{res}
{txt}modelG4Abzloyal[9,1]
            r2vs1.
              _at
     b {res} 426.18626
{txt}    se {res} 188.03832
{txt}     z {res} 2.2664862
{txt}pvalue {res} .02342163
{txt}    ll {res} 57.637918
{txt}    ul {res} 794.73459
{txt}    df {res}         .
{txt}  crit {res}  1.959964
{txt} eform {res}         0
{reset}
{com}. 
. 
. 
. 
. 
. 
. ******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. **** MODEL G4B: WEIBULL MODEL [INCLUSION OF BOTH AGENCY AND PRESIDENTIAL ADMINISTRATION FIXED EFFECTS: CLUSTER-ADJUSTED STANDARD ERRORS BY AGENCY] ****
. 
. streg   c.zloyalmedian##i.soubinaryagency2nom  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp okstartunemployment  i.okstartadyr  i.sbagency reagan bush41 clinton bush43 if okstartadyr!=1, distribution(weibull) hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur
{txt}note: 27.sbagency omitted because of collinearity
note: 57.sbagency omitted because of collinearity
note: 61.sbagency omitted because of collinearity

Fitting constant-only model:

Iteration 0:   log pseudolikelihood = {res} -586.2933
{txt}Iteration 1:   log pseudolikelihood = {res}-491.77185
{txt}Iteration 2:   log pseudolikelihood = {res} -490.0111
{txt}Iteration 3:   log pseudolikelihood = {res}-490.01096
{txt}Iteration 4:   log pseudolikelihood = {res}-490.01096

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-490.01096}  
Iteration 1:{space 3}log pseudolikelihood = {res:-355.63308}  
Iteration 2:{space 3}log pseudolikelihood = {res:-279.60491}  
Iteration 3:{space 3}log pseudolikelihood = {res:-279.10674}  
Iteration 4:{space 3}log pseudolikelihood = {res:-279.10624}  
Iteration 5:{space 3}log pseudolikelihood = {res:-279.10624}  
{res}
{txt}Weibull PH regression

No. of subjects      = {res}         490             {txt}Number of obs    =  {res}       490
{txt}No. of failures      = {res}         490
{txt}Time at risk         = {res}      416913
{col 49}{help j_robustsingular##|_new:Wald chi2(21)}{txt}{col 66}=  {res}         .
{txt}Log pseudolikelihood =   {res}-279.10624             {txt}Prob > chi2      =  {res}         .

{txt}{ralign 100:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}                                _t{col 36}{c |} Haz. Ratio{col 48}   Std. Err.{col 60}      z{col 68}   P>|z|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}zloyalmedian {c |}{col 36}{res}{space 2} 1.292428{col 48}{space 2} .2574578{col 59}{space 1}    1.29{col 68}{space 3}0.198{col 76}{space 4} .8746676{col 89}{space 3}  1.90972
{txt}{space 13}1.soubinaryagency2nom {c |}{col 36}{res}{space 2} 1.308932{col 48}{space 2}  .299879{col 59}{space 1}    1.18{col 68}{space 3}0.240{col 76}{space 4} .8354201{col 89}{space 3} 2.050827
{txt}{space 34} {c |}
soubinaryagency2nom#c.zloyalmedian {c |}
{space 32}1  {c |}{col 36}{res}{space 2} .6192014{col 48}{space 2} .1378128{col 59}{space 1}   -2.15{col 68}{space 3}0.031{col 76}{space 4} .4002984{col 89}{space 3} .9578113
{txt}{space 34} {c |}
{space 21}zpecompmedian {c |}{col 36}{res}{space 2} 1.012885{col 48}{space 2} .1038799{col 59}{space 1}    0.12{col 68}{space 3}0.901{col 76}{space 4} .8284418{col 89}{space 3} 1.238391
{txt}{space 21}zmecompmedian {c |}{col 36}{res}{space 2} 1.048317{col 48}{space 2} .1249291{col 59}{space 1}    0.40{col 68}{space 3}0.692{col 76}{space 4} .8299541{col 89}{space 3} 1.324132
{txt}{space 25}toplevel2 {c |}{col 36}{res}{space 2} .4805053{col 48}{space 2}  .075781{col 59}{space 1}   -4.65{col 68}{space 3}0.000{col 76}{space 4} .3527397{col 89}{space 3} .6545488
{txt}{space 14}presagencyideolalign {c |}{col 36}{res}{space 2} .6037951{col 48}{space 2}  .267003{col 59}{space 1}   -1.14{col 68}{space 3}0.254{col 76}{space 4} .2537941{col 89}{space 3} 1.436474
{txt}{space 12}presagencyideolopposed {c |}{col 36}{res}{space 2} .5189511{col 48}{space 2} .2304767{col 59}{space 1}   -1.48{col 68}{space 3}0.140{col 76}{space 4} .2173155{col 89}{space 3}  1.23926
{txt}{space 19}subagencydesign {c |}{col 36}{res}{space 2} 2.114614{col 48}{space 2}  .711035{col 59}{space 1}    2.23{col 68}{space 3}0.026{col 76}{space 4} 1.093997{col 89}{space 3}  4.08739
{txt}{space 12}standaloneagencydesign {c |}{col 36}{res}{space 2} 1.766047{col 48}{space 2} .8183322{col 59}{space 1}    1.23{col 68}{space 3}0.220{col 76}{space 4} .7121665{col 89}{space 3} 4.379485
{txt}{space 8}okstartsenpolarizationmean {c |}{col 36}{res}{space 2} 5.74e-19{col 48}{space 2} 8.30e-18{col 59}{space 1}   -2.90{col 68}{space 3}0.004{col 76}{space 4} 2.79e-31{col 89}{space 3} 1.18e-06
{txt}{space 11}okstartfilipresdistance {c |}{col 36}{res}{space 2} 7638.728{col 48}{space 2} 27925.89{col 59}{space 1}    2.45{col 68}{space 3}0.014{col 76}{space 4} 5.904361{col 89}{space 3}  9882554
{txt}{space 23}okcrossover {c |}{col 36}{res}{space 2} .1581545{col 48}{space 2} .0438578{col 59}{space 1}   -6.65{col 68}{space 3}0.000{col 76}{space 4} .0918407{col 89}{space 3} .2723506
{txt}{space 20}okstartpresapp {c |}{col 36}{res}{space 2}  .988976{col 48}{space 2} .0096733{col 59}{space 1}   -1.13{col 68}{space 3}0.257{col 76}{space 4} .9701973{col 89}{space 3} 1.008118
{txt}{space 15}okstartunemployment {c |}{col 36}{res}{space 2} 1.449777{col 48}{space 2} .1927213{col 59}{space 1}    2.79{col 68}{space 3}0.005{col 76}{space 4} 1.117248{col 89}{space 3} 1.881277
{txt}{space 34} {c |}
{space 23}okstartadyr {c |}
{space 32}3  {c |}{col 36}{res}{space 2} 2.391743{col 48}{space 2} .4469575{col 59}{space 1}    4.67{col 68}{space 3}0.000{col 76}{space 4} 1.658235{col 89}{space 3} 3.449713
{txt}{space 32}4  {c |}{col 36}{res}{space 2} 2.412504{col 48}{space 2} .9208412{col 59}{space 1}    2.31{col 68}{space 3}0.021{col 76}{space 4} 1.141743{col 89}{space 3} 5.097622
{txt}{space 32}5  {c |}{col 36}{res}{space 2} 2.031252{col 48}{space 2}  .875653{col 59}{space 1}    1.64{col 68}{space 3}0.100{col 76}{space 4}  .872608{col 89}{space 3} 4.728338
{txt}{space 32}6  {c |}{col 36}{res}{space 2} 5.072403{col 48}{space 2} 2.309911{col 59}{space 1}    3.57{col 68}{space 3}0.000{col 76}{space 4} 2.077719{col 89}{space 3} 12.38342
{txt}{space 32}7  {c |}{col 36}{res}{space 2} 8.815159{col 48}{space 2} 4.899249{col 59}{space 1}    3.92{col 68}{space 3}0.000{col 76}{space 4} 2.965878{col 89}{space 3} 26.20035
{txt}{space 32}8  {c |}{col 36}{res}{space 2} 16.35316{col 48}{space 2} 9.189069{col 59}{space 1}    4.97{col 68}{space 3}0.000{col 76}{space 4} 5.436251{col 89}{space 3} 49.19305
{txt}{space 34} {c |}
{space 26}sbagency {c |}
{space 32}2  {c |}{col 36}{res}{space 2} 3.551318{col 48}{space 2}  1.68228{col 59}{space 1}    2.68{col 68}{space 3}0.007{col 76}{space 4} 1.403365{col 89}{space 3} 8.986873
{txt}{space 32}3  {c |}{col 36}{res}{space 2} 2.756932{col 48}{space 2} 1.224771{col 59}{space 1}    2.28{col 68}{space 3}0.022{col 76}{space 4} 1.154194{col 89}{space 3} 6.585268
{txt}{space 32}4  {c |}{col 36}{res}{space 2} 2.557178{col 48}{space 2} .6283493{col 59}{space 1}    3.82{col 68}{space 3}0.000{col 76}{space 4} 1.579806{col 89}{space 3} 4.139215
{txt}{space 32}5  {c |}{col 36}{res}{space 2} .6727209{col 48}{space 2} .2972762{col 59}{space 1}   -0.90{col 68}{space 3}0.370{col 76}{space 4} .2829358{col 89}{space 3} 1.599491
{txt}{space 32}6  {c |}{col 36}{res}{space 2} 3.371647{col 48}{space 2} 1.037958{col 59}{space 1}    3.95{col 68}{space 3}0.000{col 76}{space 4} 1.844169{col 89}{space 3} 6.164296
{txt}{space 32}7  {c |}{col 36}{res}{space 2} 1.963053{col 48}{space 2} .8888273{col 59}{space 1}    1.49{col 68}{space 3}0.136{col 76}{space 4} .8082142{col 89}{space 3} 4.768013
{txt}{space 32}8  {c |}{col 36}{res}{space 2} 2.484524{col 48}{space 2} 1.104072{col 59}{space 1}    2.05{col 68}{space 3}0.041{col 76}{space 4} 1.039888{col 89}{space 3} 5.936082
{txt}{space 32}9  {c |}{col 36}{res}{space 2}  3.40939{col 48}{space 2} 1.361213{col 59}{space 1}    3.07{col 68}{space 3}0.002{col 76}{space 4} 1.558945{col 89}{space 3} 7.456283
{txt}{space 31}11  {c |}{col 36}{res}{space 2} 4.355518{col 48}{space 2}  2.16192{col 59}{space 1}    2.96{col 68}{space 3}0.003{col 76}{space 4} 1.646396{col 89}{space 3} 11.52246
{txt}{space 31}12  {c |}{col 36}{res}{space 2} 2.090656{col 48}{space 2} .6890462{col 59}{space 1}    2.24{col 68}{space 3}0.025{col 76}{space 4} 1.095823{col 89}{space 3} 3.988639
{txt}{space 31}13  {c |}{col 36}{res}{space 2} 3.123965{col 48}{space 2} 1.475459{col 59}{space 1}    2.41{col 68}{space 3}0.016{col 76}{space 4} 1.237887{col 89}{space 3} 7.883723
{txt}{space 31}14  {c |}{col 36}{res}{space 2} 3.515692{col 48}{space 2}  1.68295{col 59}{space 1}    2.63{col 68}{space 3}0.009{col 76}{space 4} 1.375762{col 89}{space 3} 8.984176
{txt}{space 31}15  {c |}{col 36}{res}{space 2} 2.028349{col 48}{space 2} .8892115{col 59}{space 1}    1.61{col 68}{space 3}0.107{col 76}{space 4} .8589798{col 89}{space 3} 4.789635
{txt}{space 31}16  {c |}{col 36}{res}{space 2} .5179022{col 48}{space 2}  .163454{col 59}{space 1}   -2.08{col 68}{space 3}0.037{col 76}{space 4} .2789985{col 89}{space 3} .9613767
{txt}{space 31}17  {c |}{col 36}{res}{space 2} 1.690607{col 48}{space 2} .3037379{col 59}{space 1}    2.92{col 68}{space 3}0.003{col 76}{space 4} 1.188814{col 89}{space 3} 2.404203
{txt}{space 31}18  {c |}{col 36}{res}{space 2} 2.296973{col 48}{space 2} 1.026349{col 59}{space 1}    1.86{col 68}{space 3}0.063{col 76}{space 4} .9567892{col 89}{space 3} 5.514364
{txt}{space 31}19  {c |}{col 36}{res}{space 2} 1.122002{col 48}{space 2} .2097451{col 59}{space 1}    0.62{col 68}{space 3}0.538{col 76}{space 4} .7778061{col 89}{space 3} 1.618511
{txt}{space 31}20  {c |}{col 36}{res}{space 2}  .200222{col 48}{space 2} .0990756{col 59}{space 1}   -3.25{col 68}{space 3}0.001{col 76}{space 4} .0759124{col 89}{space 3} .5280936
{txt}{space 31}21  {c |}{col 36}{res}{space 2} 1.153252{col 48}{space 2} .2582569{col 59}{space 1}    0.64{col 68}{space 3}0.524{col 76}{space 4} .7435462{col 89}{space 3} 1.788713
{txt}{space 31}22  {c |}{col 36}{res}{space 2} 1.236682{col 48}{space 2} .5836773{col 59}{space 1}    0.45{col 68}{space 3}0.653{col 76}{space 4} .4903615{col 89}{space 3} 3.118889
{txt}{space 31}23  {c |}{col 36}{res}{space 2} .9860987{col 48}{space 2} .4315129{col 59}{space 1}   -0.03{col 68}{space 3}0.974{col 76}{space 4} .4182519{col 89}{space 3} 2.324892
{txt}{space 31}24  {c |}{col 36}{res}{space 2} .2328329{col 48}{space 2} .2104774{col 59}{space 1}   -1.61{col 68}{space 3}0.107{col 76}{space 4} .0395887{col 89}{space 3}  1.36936
{txt}{space 31}25  {c |}{col 36}{res}{space 2} 1.748102{col 48}{space 2} .5065895{col 59}{space 1}    1.93{col 68}{space 3}0.054{col 76}{space 4}  .990589{col 89}{space 3} 3.084891
{txt}{space 31}26  {c |}{col 36}{res}{space 2} .8784068{col 48}{space 2} .2054378{col 59}{space 1}   -0.55{col 68}{space 3}0.579{col 76}{space 4} .5554186{col 89}{space 3}  1.38922
{txt}{space 31}27  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 31}28  {c |}{col 36}{res}{space 2} 1.988909{col 48}{space 2} .4030325{col 59}{space 1}    3.39{col 68}{space 3}0.001{col 76}{space 4} 1.336988{col 89}{space 3}  2.95871
{txt}{space 31}29  {c |}{col 36}{res}{space 2} 4.363036{col 48}{space 2} 2.320247{col 59}{space 1}    2.77{col 68}{space 3}0.006{col 76}{space 4}  1.53859{col 89}{space 3} 12.37242
{txt}{space 31}30  {c |}{col 36}{res}{space 2}  2.56429{col 48}{space 2} 1.333306{col 59}{space 1}    1.81{col 68}{space 3}0.070{col 76}{space 4} .9255159{col 89}{space 3} 7.104776
{txt}{space 31}50  {c |}{col 36}{res}{space 2} 2.206229{col 48}{space 2} .6801583{col 59}{space 1}    2.57{col 68}{space 3}0.010{col 76}{space 4} 1.205685{col 89}{space 3} 4.037081
{txt}{space 31}51  {c |}{col 36}{res}{space 2} 2.812155{col 48}{space 2} .8299281{col 59}{space 1}    3.50{col 68}{space 3}0.000{col 76}{space 4} 1.576998{col 89}{space 3} 5.014728
{txt}{space 31}52  {c |}{col 36}{res}{space 2} 1.606434{col 48}{space 2} .5811274{col 59}{space 1}    1.31{col 68}{space 3}0.190{col 76}{space 4} .7905707{col 89}{space 3} 3.264262
{txt}{space 31}53  {c |}{col 36}{res}{space 2} 1.946025{col 48}{space 2} .5887734{col 59}{space 1}    2.20{col 68}{space 3}0.028{col 76}{space 4} 1.075514{col 89}{space 3} 3.521122
{txt}{space 31}54  {c |}{col 36}{res}{space 2} 1.812063{col 48}{space 2} .5698075{col 59}{space 1}    1.89{col 68}{space 3}0.059{col 76}{space 4} .9783879{col 89}{space 3} 3.356106
{txt}{space 31}55  {c |}{col 36}{res}{space 2} 1.033759{col 48}{space 2} .4208297{col 59}{space 1}    0.08{col 68}{space 3}0.935{col 76}{space 4} .4654861{col 89}{space 3} 2.295791
{txt}{space 31}56  {c |}{col 36}{res}{space 2} .6478781{col 48}{space 2} .3205974{col 59}{space 1}   -0.88{col 68}{space 3}0.380{col 76}{space 4} .2456308{col 89}{space 3} 1.708849
{txt}{space 31}57  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 31}58  {c |}{col 36}{res}{space 2} 1.490946{col 48}{space 2} .8045023{col 59}{space 1}    0.74{col 68}{space 3}0.459{col 76}{space 4} .5177984{col 89}{space 3} 4.293024
{txt}{space 31}59  {c |}{col 36}{res}{space 2} .5892169{col 48}{space 2} .3072448{col 59}{space 1}   -1.01{col 68}{space 3}0.310{col 76}{space 4} .2120409{col 89}{space 3}  1.63731
{txt}{space 31}60  {c |}{col 36}{res}{space 2} 1.061028{col 48}{space 2} .3356186{col 59}{space 1}    0.19{col 68}{space 3}0.851{col 76}{space 4} .5707939{col 89}{space 3} 1.972306
{txt}{space 31}61  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 34} {c |}
{space 28}reagan {c |}{col 36}{res}{space 2} .0123257{col 48}{space 2} .0188187{col 59}{space 1}   -2.88{col 68}{space 3}0.004{col 76}{space 4} .0006183{col 89}{space 3} .2457067
{txt}{space 28}bush41 {c |}{col 36}{res}{space 2} .1418065{col 48}{space 2} .1551826{col 59}{space 1}   -1.78{col 68}{space 3}0.074{col 76}{space 4} .0166037{col 89}{space 3} 1.211124
{txt}{space 27}clinton {c |}{col 36}{res}{space 2} .9379674{col 48}{space 2} .8976108{col 59}{space 1}   -0.07{col 68}{space 3}0.947{col 76}{space 4} .1437506{col 89}{space 3} 6.120203
{txt}{space 28}bush43 {c |}{col 36}{res}{space 2}   .22724{col 48}{space 2} .3060614{col 59}{space 1}   -1.10{col 68}{space 3}0.271{col 76}{space 4} .0162193{col 89}{space 3} 3.183738
{txt}{space 29}_cons {c |}{col 36}{res}{space 2} 2.623741{col 48}{space 2} 20.09046{col 59}{space 1}    0.13{col 68}{space 3}0.900{col 76}{space 4} 7.96e-07{col 89}{space 3}  8644205
{txt}{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 29}/ln_p {c |}{col 36}{res}{space 2} 1.006962{col 48}{space 2} .0446499{col 59}{space 1}   22.55{col 68}{space 3}0.000{col 76}{space 4} .9194499{col 89}{space 3} 1.094474
{txt}{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                                 p {c |}{col 36}{res}{space 2} 2.737273{col 48}{space 2} .1222189{col 76}{space 4}  2.50791{col 89}{space 3} 2.987612
{txt}                               1/p {c |}{col 36}{res}{space 2} .3653271{col 48}{space 2} .0163118{col 76}{space 4} .3347155{col 89}{space 3} .3987383
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline hazard{txt}.{p_end}

{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       490{col 28} -490.011{col 39}-279.1062{col 50}    23{col 58} 604.2125{col 69} 700.6838
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. estimates store modelG4B
{txt}
{com}. estout modelG4B, cells(b(star fmt(3)) se(par fmt(3))) eform
{res}
{txt}{hline 28}
{txt}                 modelG4B   
{txt}                     b/se   
{txt}{hline 28}
{res}_t                          {txt}
{txt}zloyalmedian{res}        1.292   {txt}
            {res}      (0.257)   {txt}
{txt}0.soubinar~m{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}1.soubinar~m{res}        1.309   {txt}
            {res}      (0.300)   {txt}
{txt}0.soubinar~i{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}1.soubinar~i{res}        0.619*  {txt}
            {res}      (0.138)   {txt}
{txt}zpecompmed~n{res}        1.013   {txt}
            {res}      (0.104)   {txt}
{txt}zmecompmed~n{res}        1.048   {txt}
            {res}      (0.125)   {txt}
{txt}toplevel2   {res}        0.481***{txt}
            {res}      (0.076)   {txt}
{txt}presagency~n{res}        0.604   {txt}
            {res}      (0.267)   {txt}
{txt}presagency~d{res}        0.519   {txt}
            {res}      (0.230)   {txt}
{txt}subagencyd~n{res}        2.115*  {txt}
            {res}      (0.711)   {txt}
{txt}standalone~n{res}        1.766   {txt}
            {res}      (0.818)   {txt}
{txt}okstartsen~n{res}        0.000** {txt}
            {res}      (0.000)   {txt}
{txt}okstartfil~e{res}     7638.728*  {txt}
            {res}  (27925.892)   {txt}
{txt}okcrossover {res}        0.158***{txt}
            {res}      (0.044)   {txt}
{txt}okstartpre~p{res}        0.989   {txt}
            {res}      (0.010)   {txt}
{txt}okstartune~t{res}        1.450** {txt}
            {res}      (0.193)   {txt}
{txt}2.okstarta~r{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}3.okstarta~r{res}        2.392***{txt}
            {res}      (0.447)   {txt}
{txt}4.okstarta~r{res}        2.413*  {txt}
            {res}      (0.921)   {txt}
{txt}5.okstarta~r{res}        2.031   {txt}
            {res}      (0.876)   {txt}
{txt}6.okstarta~r{res}        5.072***{txt}
            {res}      (2.310)   {txt}
{txt}7.okstarta~r{res}        8.815***{txt}
            {res}      (4.899)   {txt}
{txt}8.okstarta~r{res}       16.353***{txt}
            {res}      (9.189)   {txt}
{txt}1.sbagency  {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}2.sbagency  {res}        3.551** {txt}
            {res}      (1.682)   {txt}
{txt}3.sbagency  {res}        2.757*  {txt}
            {res}      (1.225)   {txt}
{txt}4.sbagency  {res}        2.557***{txt}
            {res}      (0.628)   {txt}
{txt}5.sbagency  {res}        0.673   {txt}
            {res}      (0.297)   {txt}
{txt}6.sbagency  {res}        3.372***{txt}
            {res}      (1.038)   {txt}
{txt}7.sbagency  {res}        1.963   {txt}
            {res}      (0.889)   {txt}
{txt}8.sbagency  {res}        2.485*  {txt}
            {res}      (1.104)   {txt}
{txt}9.sbagency  {res}        3.409** {txt}
            {res}      (1.361)   {txt}
{txt}11.sbagency {res}        4.356** {txt}
            {res}      (2.162)   {txt}
{txt}12.sbagency {res}        2.091*  {txt}
            {res}      (0.689)   {txt}
{txt}13.sbagency {res}        3.124*  {txt}
            {res}      (1.475)   {txt}
{txt}14.sbagency {res}        3.516** {txt}
            {res}      (1.683)   {txt}
{txt}15.sbagency {res}        2.028   {txt}
            {res}      (0.889)   {txt}
{txt}16.sbagency {res}        0.518*  {txt}
            {res}      (0.163)   {txt}
{txt}17.sbagency {res}        1.691** {txt}
            {res}      (0.304)   {txt}
{txt}18.sbagency {res}        2.297   {txt}
            {res}      (1.026)   {txt}
{txt}19.sbagency {res}        1.122   {txt}
            {res}      (0.210)   {txt}
{txt}20.sbagency {res}        0.200** {txt}
            {res}      (0.099)   {txt}
{txt}21.sbagency {res}        1.153   {txt}
            {res}      (0.258)   {txt}
{txt}22.sbagency {res}        1.237   {txt}
            {res}      (0.584)   {txt}
{txt}23.sbagency {res}        0.986   {txt}
            {res}      (0.432)   {txt}
{txt}24.sbagency {res}        0.233   {txt}
            {res}      (0.210)   {txt}
{txt}25.sbagency {res}        1.748   {txt}
            {res}      (0.507)   {txt}
{txt}26.sbagency {res}        0.878   {txt}
            {res}      (0.205)   {txt}
{txt}27.sbagency {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}28.sbagency {res}        1.989***{txt}
            {res}      (0.403)   {txt}
{txt}29.sbagency {res}        4.363** {txt}
            {res}      (2.320)   {txt}
{txt}30.sbagency {res}        2.564   {txt}
            {res}      (1.333)   {txt}
{txt}50.sbagency {res}        2.206*  {txt}
            {res}      (0.680)   {txt}
{txt}51.sbagency {res}        2.812***{txt}
            {res}      (0.830)   {txt}
{txt}52.sbagency {res}        1.606   {txt}
            {res}      (0.581)   {txt}
{txt}53.sbagency {res}        1.946*  {txt}
            {res}      (0.589)   {txt}
{txt}54.sbagency {res}        1.812   {txt}
            {res}      (0.570)   {txt}
{txt}55.sbagency {res}        1.034   {txt}
            {res}      (0.421)   {txt}
{txt}56.sbagency {res}        0.648   {txt}
            {res}      (0.321)   {txt}
{txt}57.sbagency {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}58.sbagency {res}        1.491   {txt}
            {res}      (0.805)   {txt}
{txt}59.sbagency {res}        0.589   {txt}
            {res}      (0.307)   {txt}
{txt}60.sbagency {res}        1.061   {txt}
            {res}      (0.336)   {txt}
{txt}61.sbagency {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}reagan      {res}        0.012** {txt}
            {res}      (0.019)   {txt}
{txt}bush41      {res}        0.142   {txt}
            {res}      (0.155)   {txt}
{txt}clinton     {res}        0.938   {txt}
            {res}      (0.898)   {txt}
{txt}bush43      {res}        0.227   {txt}
            {res}      (0.306)   {txt}
{txt}_cons       {res}        2.624   {txt}
            {res}     (20.090)   {txt}
{txt}{hline 28}
{res}/                           {txt}
{txt}ln_p        {res}        2.737***{txt}
            {res}      (0.122)   {txt}
{txt}{hline 28}

{com}. 
. 
. 
. *** COMPUTE Figure G1: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the HAZARD RATIO of APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [MG1B−MG4B] × 1 Horizontal Point Estimates and 95% CIs{c )-}{c )-}. ****
. ** NOTE: IQR =  0.7979161  [0.3859205 - (-0.4119956)] *** 
. 
. lincomest 1.soubinaryagency2nom#c.zloyalmedian*0.7979161, eform(hr)
{txt}Confidence interval for formula:
{res}1.soubinaryagency2nom#c.zloyalmedian*0.7979161

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .6821806{col 26}{space 2} .1211474{col 37}{space 1}   -2.15{col 46}{space 3}0.031{col 54}{space 4} .4816546{col 67}{space 3} .9661909
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelG4Bzloyal = r(table)
{txt}
{com}. mat list modelG4Bzloyal
{res}
{txt}modelG4Bzloyal[9,1]
               (1)
     b {res}  .68218056
{txt}    se {res}  .12114744
{txt}     z {res} -2.1536353
{txt}pvalue {res}  .03126878
{txt}    ll {res}   .4816546
{txt}    ul {res}  .96619094
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          1
{reset}
{com}. 
. 
. 
. 
. **** COMPUTE Figure G2: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the MEDIAN NUMBER OF DAYS OF APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [MG1B−MG4B] × 1 Horizontal Point Estimates and 95% CIs{c )-}.
. 
. 
. ** Re-Estimate Model G4B  with 'manual' interaction variable **
. streg   zloyalmedian soubinaryagency2nom loyalppdiff  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp okstartunemployment  i.okstartadyr i.sbagency reagan bush41 clinton bush43 if okstartadyr!=1, distribution(weibull) hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur
{txt}note: 27.sbagency omitted because of collinearity
note: 57.sbagency omitted because of collinearity
note: 61.sbagency omitted because of collinearity

Fitting constant-only model:

Iteration 0:   log pseudolikelihood = {res} -586.2933
{txt}Iteration 1:   log pseudolikelihood = {res}-491.77185
{txt}Iteration 2:   log pseudolikelihood = {res} -490.0111
{txt}Iteration 3:   log pseudolikelihood = {res}-490.01096
{txt}Iteration 4:   log pseudolikelihood = {res}-490.01096

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-490.01096}  
Iteration 1:{space 3}log pseudolikelihood = {res:-355.63308}  
Iteration 2:{space 3}log pseudolikelihood = {res:-279.60491}  
Iteration 3:{space 3}log pseudolikelihood = {res:-279.10674}  
Iteration 4:{space 3}log pseudolikelihood = {res:-279.10624}  
Iteration 5:{space 3}log pseudolikelihood = {res:-279.10624}  
{res}
{txt}Weibull PH regression

No. of subjects      = {res}         490             {txt}Number of obs    =  {res}       490
{txt}No. of failures      = {res}         490
{txt}Time at risk         = {res}      416913
{col 49}{help j_robustsingular##|_new:Wald chi2(21)}{txt}{col 66}=  {res}         .
{txt}Log pseudolikelihood =   {res}-279.10624             {txt}Prob > chi2      =  {res}         .

{txt}{ralign 92:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}                        _t{col 28}{c |} Haz. Ratio{col 40}   Std. Err.{col 52}      z{col 60}   P>|z|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}zloyalmedian {c |}{col 28}{res}{space 2} 1.292428{col 40}{space 2} .2574578{col 51}{space 1}    1.29{col 60}{space 3}0.198{col 68}{space 4} .8746676{col 81}{space 3}  1.90972
{txt}{space 7}soubinaryagency2nom {c |}{col 28}{res}{space 2} 1.308932{col 40}{space 2}  .299879{col 51}{space 1}    1.18{col 60}{space 3}0.240{col 68}{space 4} .8354201{col 81}{space 3} 2.050827
{txt}{space 15}loyalppdiff {c |}{col 28}{res}{space 2} .6192014{col 40}{space 2} .1378128{col 51}{space 1}   -2.15{col 60}{space 3}0.031{col 68}{space 4} .4002984{col 81}{space 3} .9578113
{txt}{space 13}zpecompmedian {c |}{col 28}{res}{space 2} 1.012885{col 40}{space 2} .1038799{col 51}{space 1}    0.12{col 60}{space 3}0.901{col 68}{space 4} .8284418{col 81}{space 3} 1.238391
{txt}{space 13}zmecompmedian {c |}{col 28}{res}{space 2} 1.048317{col 40}{space 2} .1249291{col 51}{space 1}    0.40{col 60}{space 3}0.692{col 68}{space 4} .8299541{col 81}{space 3} 1.324132
{txt}{space 17}toplevel2 {c |}{col 28}{res}{space 2} .4805053{col 40}{space 2}  .075781{col 51}{space 1}   -4.65{col 60}{space 3}0.000{col 68}{space 4} .3527397{col 81}{space 3} .6545488
{txt}{space 6}presagencyideolalign {c |}{col 28}{res}{space 2} .6037951{col 40}{space 2}  .267003{col 51}{space 1}   -1.14{col 60}{space 3}0.254{col 68}{space 4} .2537941{col 81}{space 3} 1.436474
{txt}{space 4}presagencyideolopposed {c |}{col 28}{res}{space 2} .5189511{col 40}{space 2} .2304767{col 51}{space 1}   -1.48{col 60}{space 3}0.140{col 68}{space 4} .2173155{col 81}{space 3}  1.23926
{txt}{space 11}subagencydesign {c |}{col 28}{res}{space 2} 2.114614{col 40}{space 2}  .711035{col 51}{space 1}    2.23{col 60}{space 3}0.026{col 68}{space 4} 1.093997{col 81}{space 3}  4.08739
{txt}{space 4}standaloneagencydesign {c |}{col 28}{res}{space 2} 1.766047{col 40}{space 2} .8183322{col 51}{space 1}    1.23{col 60}{space 3}0.220{col 68}{space 4} .7121665{col 81}{space 3} 4.379485
{txt}okstartsenpolarizationmean {c |}{col 28}{res}{space 2} 5.74e-19{col 40}{space 2} 8.30e-18{col 51}{space 1}   -2.90{col 60}{space 3}0.004{col 68}{space 4} 2.79e-31{col 81}{space 3} 1.18e-06
{txt}{space 3}okstartfilipresdistance {c |}{col 28}{res}{space 2} 7638.728{col 40}{space 2} 27925.89{col 51}{space 1}    2.45{col 60}{space 3}0.014{col 68}{space 4} 5.904361{col 81}{space 3}  9882554
{txt}{space 15}okcrossover {c |}{col 28}{res}{space 2} .1581545{col 40}{space 2} .0438578{col 51}{space 1}   -6.65{col 60}{space 3}0.000{col 68}{space 4} .0918407{col 81}{space 3} .2723506
{txt}{space 12}okstartpresapp {c |}{col 28}{res}{space 2}  .988976{col 40}{space 2} .0096733{col 51}{space 1}   -1.13{col 60}{space 3}0.257{col 68}{space 4} .9701973{col 81}{space 3} 1.008118
{txt}{space 7}okstartunemployment {c |}{col 28}{res}{space 2} 1.449777{col 40}{space 2} .1927213{col 51}{space 1}    2.79{col 60}{space 3}0.005{col 68}{space 4} 1.117248{col 81}{space 3} 1.881277
{txt}{space 26} {c |}
{space 15}okstartadyr {c |}
{space 24}3  {c |}{col 28}{res}{space 2} 2.391743{col 40}{space 2} .4469575{col 51}{space 1}    4.67{col 60}{space 3}0.000{col 68}{space 4} 1.658235{col 81}{space 3} 3.449713
{txt}{space 24}4  {c |}{col 28}{res}{space 2} 2.412504{col 40}{space 2} .9208412{col 51}{space 1}    2.31{col 60}{space 3}0.021{col 68}{space 4} 1.141743{col 81}{space 3} 5.097622
{txt}{space 24}5  {c |}{col 28}{res}{space 2} 2.031252{col 40}{space 2}  .875653{col 51}{space 1}    1.64{col 60}{space 3}0.100{col 68}{space 4}  .872608{col 81}{space 3} 4.728338
{txt}{space 24}6  {c |}{col 28}{res}{space 2} 5.072403{col 40}{space 2} 2.309911{col 51}{space 1}    3.57{col 60}{space 3}0.000{col 68}{space 4} 2.077719{col 81}{space 3} 12.38342
{txt}{space 24}7  {c |}{col 28}{res}{space 2} 8.815159{col 40}{space 2} 4.899249{col 51}{space 1}    3.92{col 60}{space 3}0.000{col 68}{space 4} 2.965878{col 81}{space 3} 26.20035
{txt}{space 24}8  {c |}{col 28}{res}{space 2} 16.35316{col 40}{space 2} 9.189069{col 51}{space 1}    4.97{col 60}{space 3}0.000{col 68}{space 4} 5.436251{col 81}{space 3} 49.19305
{txt}{space 26} {c |}
{space 18}sbagency {c |}
{space 24}2  {c |}{col 28}{res}{space 2} 3.551318{col 40}{space 2}  1.68228{col 51}{space 1}    2.68{col 60}{space 3}0.007{col 68}{space 4} 1.403365{col 81}{space 3} 8.986873
{txt}{space 24}3  {c |}{col 28}{res}{space 2} 2.756932{col 40}{space 2} 1.224771{col 51}{space 1}    2.28{col 60}{space 3}0.022{col 68}{space 4} 1.154194{col 81}{space 3} 6.585268
{txt}{space 24}4  {c |}{col 28}{res}{space 2} 2.557178{col 40}{space 2} .6283493{col 51}{space 1}    3.82{col 60}{space 3}0.000{col 68}{space 4} 1.579806{col 81}{space 3} 4.139215
{txt}{space 24}5  {c |}{col 28}{res}{space 2} .6727209{col 40}{space 2} .2972762{col 51}{space 1}   -0.90{col 60}{space 3}0.370{col 68}{space 4} .2829358{col 81}{space 3} 1.599491
{txt}{space 24}6  {c |}{col 28}{res}{space 2} 3.371647{col 40}{space 2} 1.037958{col 51}{space 1}    3.95{col 60}{space 3}0.000{col 68}{space 4} 1.844169{col 81}{space 3} 6.164296
{txt}{space 24}7  {c |}{col 28}{res}{space 2} 1.963053{col 40}{space 2} .8888273{col 51}{space 1}    1.49{col 60}{space 3}0.136{col 68}{space 4} .8082142{col 81}{space 3} 4.768013
{txt}{space 24}8  {c |}{col 28}{res}{space 2} 2.484524{col 40}{space 2} 1.104072{col 51}{space 1}    2.05{col 60}{space 3}0.041{col 68}{space 4} 1.039888{col 81}{space 3} 5.936082
{txt}{space 24}9  {c |}{col 28}{res}{space 2}  3.40939{col 40}{space 2} 1.361213{col 51}{space 1}    3.07{col 60}{space 3}0.002{col 68}{space 4} 1.558945{col 81}{space 3} 7.456283
{txt}{space 23}11  {c |}{col 28}{res}{space 2} 4.355518{col 40}{space 2}  2.16192{col 51}{space 1}    2.96{col 60}{space 3}0.003{col 68}{space 4} 1.646396{col 81}{space 3} 11.52246
{txt}{space 23}12  {c |}{col 28}{res}{space 2} 2.090656{col 40}{space 2} .6890462{col 51}{space 1}    2.24{col 60}{space 3}0.025{col 68}{space 4} 1.095823{col 81}{space 3} 3.988639
{txt}{space 23}13  {c |}{col 28}{res}{space 2} 3.123965{col 40}{space 2} 1.475459{col 51}{space 1}    2.41{col 60}{space 3}0.016{col 68}{space 4} 1.237887{col 81}{space 3} 7.883723
{txt}{space 23}14  {c |}{col 28}{res}{space 2} 3.515692{col 40}{space 2}  1.68295{col 51}{space 1}    2.63{col 60}{space 3}0.009{col 68}{space 4} 1.375762{col 81}{space 3} 8.984176
{txt}{space 23}15  {c |}{col 28}{res}{space 2} 2.028349{col 40}{space 2} .8892115{col 51}{space 1}    1.61{col 60}{space 3}0.107{col 68}{space 4} .8589798{col 81}{space 3} 4.789635
{txt}{space 23}16  {c |}{col 28}{res}{space 2} .5179022{col 40}{space 2}  .163454{col 51}{space 1}   -2.08{col 60}{space 3}0.037{col 68}{space 4} .2789985{col 81}{space 3} .9613767
{txt}{space 23}17  {c |}{col 28}{res}{space 2} 1.690607{col 40}{space 2} .3037379{col 51}{space 1}    2.92{col 60}{space 3}0.003{col 68}{space 4} 1.188814{col 81}{space 3} 2.404203
{txt}{space 23}18  {c |}{col 28}{res}{space 2} 2.296973{col 40}{space 2} 1.026349{col 51}{space 1}    1.86{col 60}{space 3}0.063{col 68}{space 4} .9567892{col 81}{space 3} 5.514364
{txt}{space 23}19  {c |}{col 28}{res}{space 2} 1.122002{col 40}{space 2} .2097451{col 51}{space 1}    0.62{col 60}{space 3}0.538{col 68}{space 4} .7778061{col 81}{space 3} 1.618511
{txt}{space 23}20  {c |}{col 28}{res}{space 2}  .200222{col 40}{space 2} .0990756{col 51}{space 1}   -3.25{col 60}{space 3}0.001{col 68}{space 4} .0759124{col 81}{space 3} .5280936
{txt}{space 23}21  {c |}{col 28}{res}{space 2} 1.153252{col 40}{space 2} .2582569{col 51}{space 1}    0.64{col 60}{space 3}0.524{col 68}{space 4} .7435462{col 81}{space 3} 1.788713
{txt}{space 23}22  {c |}{col 28}{res}{space 2} 1.236682{col 40}{space 2} .5836773{col 51}{space 1}    0.45{col 60}{space 3}0.653{col 68}{space 4} .4903615{col 81}{space 3} 3.118889
{txt}{space 23}23  {c |}{col 28}{res}{space 2} .9860987{col 40}{space 2} .4315129{col 51}{space 1}   -0.03{col 60}{space 3}0.974{col 68}{space 4} .4182519{col 81}{space 3} 2.324892
{txt}{space 23}24  {c |}{col 28}{res}{space 2} .2328329{col 40}{space 2} .2104774{col 51}{space 1}   -1.61{col 60}{space 3}0.107{col 68}{space 4} .0395887{col 81}{space 3}  1.36936
{txt}{space 23}25  {c |}{col 28}{res}{space 2} 1.748102{col 40}{space 2} .5065895{col 51}{space 1}    1.93{col 60}{space 3}0.054{col 68}{space 4}  .990589{col 81}{space 3} 3.084891
{txt}{space 23}26  {c |}{col 28}{res}{space 2} .8784068{col 40}{space 2} .2054378{col 51}{space 1}   -0.55{col 60}{space 3}0.579{col 68}{space 4} .5554186{col 81}{space 3}  1.38922
{txt}{space 23}27  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (omitted)
{space 23}28  {c |}{col 28}{res}{space 2} 1.988909{col 40}{space 2} .4030325{col 51}{space 1}    3.39{col 60}{space 3}0.001{col 68}{space 4} 1.336988{col 81}{space 3}  2.95871
{txt}{space 23}29  {c |}{col 28}{res}{space 2} 4.363036{col 40}{space 2} 2.320247{col 51}{space 1}    2.77{col 60}{space 3}0.006{col 68}{space 4}  1.53859{col 81}{space 3} 12.37242
{txt}{space 23}30  {c |}{col 28}{res}{space 2}  2.56429{col 40}{space 2} 1.333306{col 51}{space 1}    1.81{col 60}{space 3}0.070{col 68}{space 4} .9255159{col 81}{space 3} 7.104776
{txt}{space 23}50  {c |}{col 28}{res}{space 2} 2.206229{col 40}{space 2} .6801583{col 51}{space 1}    2.57{col 60}{space 3}0.010{col 68}{space 4} 1.205685{col 81}{space 3} 4.037081
{txt}{space 23}51  {c |}{col 28}{res}{space 2} 2.812155{col 40}{space 2} .8299281{col 51}{space 1}    3.50{col 60}{space 3}0.000{col 68}{space 4} 1.576998{col 81}{space 3} 5.014728
{txt}{space 23}52  {c |}{col 28}{res}{space 2} 1.606434{col 40}{space 2} .5811274{col 51}{space 1}    1.31{col 60}{space 3}0.190{col 68}{space 4} .7905707{col 81}{space 3} 3.264262
{txt}{space 23}53  {c |}{col 28}{res}{space 2} 1.946025{col 40}{space 2} .5887734{col 51}{space 1}    2.20{col 60}{space 3}0.028{col 68}{space 4} 1.075514{col 81}{space 3} 3.521122
{txt}{space 23}54  {c |}{col 28}{res}{space 2} 1.812063{col 40}{space 2} .5698075{col 51}{space 1}    1.89{col 60}{space 3}0.059{col 68}{space 4} .9783879{col 81}{space 3} 3.356106
{txt}{space 23}55  {c |}{col 28}{res}{space 2} 1.033759{col 40}{space 2} .4208297{col 51}{space 1}    0.08{col 60}{space 3}0.935{col 68}{space 4} .4654861{col 81}{space 3} 2.295791
{txt}{space 23}56  {c |}{col 28}{res}{space 2} .6478781{col 40}{space 2} .3205974{col 51}{space 1}   -0.88{col 60}{space 3}0.380{col 68}{space 4} .2456308{col 81}{space 3} 1.708849
{txt}{space 23}57  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (omitted)
{space 23}58  {c |}{col 28}{res}{space 2} 1.490946{col 40}{space 2} .8045023{col 51}{space 1}    0.74{col 60}{space 3}0.459{col 68}{space 4} .5177984{col 81}{space 3} 4.293024
{txt}{space 23}59  {c |}{col 28}{res}{space 2} .5892169{col 40}{space 2} .3072448{col 51}{space 1}   -1.01{col 60}{space 3}0.310{col 68}{space 4} .2120409{col 81}{space 3}  1.63731
{txt}{space 23}60  {c |}{col 28}{res}{space 2} 1.061028{col 40}{space 2} .3356186{col 51}{space 1}    0.19{col 60}{space 3}0.851{col 68}{space 4} .5707939{col 81}{space 3} 1.972306
{txt}{space 23}61  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (omitted)
{space 26} {c |}
{space 20}reagan {c |}{col 28}{res}{space 2} .0123257{col 40}{space 2} .0188187{col 51}{space 1}   -2.88{col 60}{space 3}0.004{col 68}{space 4} .0006183{col 81}{space 3} .2457067
{txt}{space 20}bush41 {c |}{col 28}{res}{space 2} .1418065{col 40}{space 2} .1551826{col 51}{space 1}   -1.78{col 60}{space 3}0.074{col 68}{space 4} .0166037{col 81}{space 3} 1.211124
{txt}{space 19}clinton {c |}{col 28}{res}{space 2} .9379674{col 40}{space 2} .8976108{col 51}{space 1}   -0.07{col 60}{space 3}0.947{col 68}{space 4} .1437506{col 81}{space 3} 6.120203
{txt}{space 20}bush43 {c |}{col 28}{res}{space 2}   .22724{col 40}{space 2} .3060614{col 51}{space 1}   -1.10{col 60}{space 3}0.271{col 68}{space 4} .0162193{col 81}{space 3} 3.183738
{txt}{space 21}_cons {c |}{col 28}{res}{space 2} 2.623741{col 40}{space 2} 20.09046{col 51}{space 1}    0.13{col 60}{space 3}0.900{col 68}{space 4} 7.96e-07{col 81}{space 3}  8644205
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}/ln_p {c |}{col 28}{res}{space 2} 1.006962{col 40}{space 2} .0446499{col 51}{space 1}   22.55{col 60}{space 3}0.000{col 68}{space 4} .9194499{col 81}{space 3} 1.094474
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                         p {c |}{col 28}{res}{space 2} 2.737273{col 40}{space 2} .1222189{col 68}{space 4}  2.50791{col 81}{space 3} 2.987612
{txt}                       1/p {c |}{col 28}{res}{space 2} .3653271{col 40}{space 2} .0163118{col 68}{space 4} .3347155{col 81}{space 3} .3987383
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline hazard{txt}.{p_end}

{com}. 
. estimates store modelG4Ba
{txt}
{com}. 
. margins, predict(median time) at(loyalppdiff=(-0.4119956  0.3859205))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       490
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.4119956}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}.3859205}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 795.4785{col 26}{space 2} 32.37795{col 37}{space 1}   24.57{col 46}{space 3}0.000{col 54}{space 4} 732.0189{col 67}{space 3} 858.9381
{txt}{space 10}2  {c |}{col 14}{res}{space 2}  914.765{col 26}{space 2} 37.62888{col 37}{space 1}   24.31{col 46}{space 3}0.000{col 54}{space 4} 841.0137{col 67}{space 3} 988.5162
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. ** Generate Differential Predicted Median Survival Time of Senate Committee Stage of Confirmation Process -- Based on Interquartile Differential [corresponding to Differential Marginal Hazard Ratio Estimates] **
. margins, predict(median time) at(loyalppdiff=(-0.4119956  0.3859205))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       490
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.4119956}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}.3859205}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     4.63{col 38}{space 2}   0.0314
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2} 119.2865{col 26}{space 2} 55.44747{col 37}{space 5} 10.61143{col 51}{space 3} 227.9615
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. matrix modelG4Bazloyal = r(table)
{txt}
{com}. mat list modelG4Bazloyal
{res}
{txt}modelG4Bazloyal[9,1]
            r2vs1.
              _at
     b {res} 119.28647
{txt}    se {res} 55.447466
{txt}     z {res} 2.1513421
{txt}pvalue {res} .03144921
{txt}    ll {res} 10.611429
{txt}    ul {res}  227.9615
{txt}    df {res}         .
{txt}  crit {res}  1.959964
{txt} eform {res}         0
{reset}
{com}. 
. 
. 
. estimates restore modelG4Ba
{txt}(results {stata estimates replay modelG4Ba:modelG4Ba} are active now)

{com}. 
. margins, predict(median time) at(loyalppdiff=(-0.6930394 1.220186))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       490
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.6930394}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}1.220186}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 757.2778{col 26}{space 2} 45.67933{col 37}{space 1}   16.58{col 46}{space 3}0.000{col 54}{space 4}  667.748{col 67}{space 3} 846.8076
{txt}{space 10}2  {c |}{col 14}{res}{space 2} 1058.656{col 26}{space 2} 109.4295{col 37}{space 1}    9.67{col 46}{space 3}0.000{col 54}{space 4} 844.1783{col 67}{space 3} 1273.134
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, predict(median time) at(loyalppdiff=(-0.6930394 1.220186))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       490
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.6930394}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}1.220186}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     4.15{col 38}{space 2}   0.0416
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2} 301.3784{col 26}{space 2} 147.9146{col 37}{space 5} 11.47124{col 51}{space 3} 591.2856
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. matrix modelG4Bbzloyal = r(table)
{txt}
{com}. mat list modelG4Bbzloyal
{res}
{txt}modelG4Bbzloyal[9,1]
            r2vs1.
              _at
     b {res} 301.37843
{txt}    se {res} 147.91455
{txt}     z {res} 2.0375171
{txt}pvalue {res} .04159825
{txt}    ll {res} 11.471238
{txt}    ul {res} 591.28563
{txt}    df {res}         .
{txt}  crit {res}  1.959964
{txt} eform {res}         0
{reset}
{com}. 
. 
. 
. 
. 
. 
. 
. 
. ***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. 
. *Figure G1
. 
. matrix pointmodel = modelG1Azloyal[1,1], modelG1Bzloyal[1,1], modelG2Azloyal[1,1], modelG2Bzloyal[1,1], modelG3Azloyal[1,1], modelG3Bzloyal[1,1], modelG4Azloyal[1,1], modelG4Bzloyal[1,1]
{txt}
{com}. 
. 
. *
. matrix cimodel = (modelG1Azloyal[5,1], modelG1Bzloyal[5,1], modelG2Azloyal[5,1], modelG2Bzloyal[5,1], modelG3Azloyal[5,1], modelG3Bzloyal[5,1], modelG4Azloyal[5,1], modelG4Bzloyal[5,1] \ modelG1Azloyal[6,1], modelG1Bzloyal[6,1], modelG2Azloyal[6,1], modelG2Bzloyal[6,1], modelG3Azloyal[6,1], modelG3Bzloyal[6,1], modelG4Azloyal[6,1], modelG4Bzloyal[6,1])
{txt}
{com}. 
. coefplot (matrix(pointmodel), ci((cimodel))), grid(none) xline(1, lcolor(red%40) lpattern(dash)) xtitle("Hazard Ratio", size(small) margin(t=2)) ylabel(1 "Model G1A"  2 "Model G1B"  3 "Model G2A" 4 "Model G2B" 5 "Model G3A" 6 "Model G3B" 7 "Model G4A" 8 "Model G4B", labsize(small) noticks) mlabel format(%9.3f) mlabposition(12) mlabsize(vsmall) xlabel(0(1)2, angle(0) labsize(small) format(%9.1f)) msymbol(o) mcolor(black) msize(small) title("FIGURE G1", size(small)) ciopts(lcolor(black)) subtitle("Marginal Differential Effect of Presidential Loyalty on Appointee Tenure Hazard" "[Policy Priority Agencies versus Non-Policy Priority Agencies]", size(small)) legend(order(17  18) lab(17 "GA Models Denote Appointees Nominated" "During the First Year of an Administration") lab(18 "GB Models Denote Appointees Not Nominated" "During the First Year of an Administration") rows(2) size(small))
{res}{txt}
{com}. 
. graph save "Graph" "C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\FigureG1.gph", replace
{txt}(note: file C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\FigureG1.gph not found)
{res}{txt}(file C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\FigureG1.gph saved)

{com}. 
. 
. 
. ***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. *Figure G2
. 
. matrix pointmodelG1 = modelG3Aazloyal[1,1], modelG3Abzloyal[1,1], modelG3Bazloyal[1,1], modelG3Bbzloyal[1,1], modelG4Aazloyal[1,1], modelG4Abzloyal[1,1], modelG4Bazloyal[1,1], modelG4Bbzloyal[1,1]
{txt}
{com}. 
. *
. matrix cimodel1 = (modelG3Aazloyal[5,1], modelG3Abzloyal[5,1], modelG3Bazloyal[5,1], modelG3Bbzloyal[5,1], modelG4Aazloyal[5,1], modelG4Abzloyal[5,1], modelG4Bazloyal[5,1], modelG4Bbzloyal[5,1] \ modelG3Aazloyal[6,1], modelG3Abzloyal[6,1], modelG3Bazloyal[6,1], modelG3Bbzloyal[6,1], modelG4Aazloyal[6,1], modelG4Abzloyal[6,1], modelG4Bazloyal[6,1], modelG4Bbzloyal[6,1])
{txt}
{com}. 
. coefplot (matrix(pointmodelG1), ci((cimodel1))), grid(none) xtitle("Predicted Number of Days", size(small) margin(t=2)) ylabel(1 `" "Model G3A" "Interquartile Change" "' 2 `" "Model G3A" "Interdecile Change" "' 3 `" "Model G3B" "Interquartile Change" "' 4 `" "Model G3B" "Interdecile Change" "' 5 `" "Model G4A" "Interquartile Change" "' 6 `" "Model G4A" "Interdecile Change" "' 7 `" "Model G4B" "Interquartile Change" "' 8 `" "Model G4B" "Interdecile Change" "', labsize(small) noticks) mlabel format(%9.0f) mlabposition(12) mlabsize(vsmall) xlabel(0(100)1000, angle(0) labsize(small) format(%9.0f))   msymbol(o) mcolor(black) msize(small) title("FIGURE G2", size(small)) ciopts(lcolor(black)) subtitle("Marginal Differential Effect of Presidential Loyalty on Median Appointee Tenure" "[Policy Priority Agencies versus Non-Policy Priority Agencies]", size(small)) legend(order(17  18) lab(17 "GA Models Denote Appointees Nominated" "During the First Year of an Administration") lab(18 "GB Models Denote Appointees Not Nominated" "During the First Year of an Administration") rows(2) size(small))
{res}{txt}
{com}. 
. graph save "Graph" "C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\FigureG2.gph", replace
{txt}(note: file C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\FigureG2.gph not found)
{res}{txt}(file C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\FigureG2.gph saved)

{com}. 
. 
. 
. 
. ***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Output\Hardwiring Committment.APPENDIX G.04-21-2023.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}22 Apr 2023, 09:56:42
{txt}{.-}
{smcl}
{txt}{sf}{ul off}